econometrics term paper example

ECON 4370: Econometrics

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An Econometric Research Paper

The format of an econometric research paper is fairly standard.   You should use the following format to create the sections of your research paper.

1. Introduction/Overview:   What is it that you are trying to explore?   Why is it relevant? What do you hope to find?   What is the structure of the remainder of the paper?

2. Literature review:   What have other researchers found about the relationship between your dependent variable and your independent variables?

3. Model: What is your theoretical model? How are the variables defined? What are the expected signs of the variables?

4. Data and Descriptive Statistics: What data did you use to estimate the theoretical model?   What problems are inherent in the data?   Provide descriptive statistics for the variables in your model using both Excel and Stata .   Minimally, you should include a table that provides the variables, their definition, mean and standard deviation for all variables included in your theoretical model.   You should also provide scatterplots , crosstabs , frequency distributions and/or pie charts of relevant variables.

5. Results:   In this section you will present the regression results for your estimated model.   You will discuss why the specification for this model is correct.   You should discuss any econometric problems that you encountered and how you resolved the problem(s).   Finally, you should interpret your results; how do your independent variables affect the dependent variable?

6. Conclusions: A summary of what you were trying to find, what you have found and any policy recommendations or suggestions for further research.  

ECONOMETRICS RESEARCH PAPER

Description and Due Dates

This research paper will apply the econometric modeling and statistical techniques learned in class to data relating to a topic of your choice.   I will expect this paper will be your best work—of professional quality.   Throughout the term we will be discussing proper writing techniques as well as proper documentation of references.   Our class website will provide links to various data sources as well as writing aids.   You will be working on this paper for the entire term (so choose a topic of interest to you!).   Listed below are interim due dates for pieces of the research.   All components of this project must be turned in on time or you will not receive credit.

1. PROPOSAL                                                                                                Due January 31 st   

In a short paragraph, you should describe the area of research that you would like to pursue in your paper. Please include possible sources for the data that you will collect.

2. ANNOTATED BIBLIOGRAPHY                                                             Due February 9 th

At least 7 articles referenced properly (use APA format).   You should also include a synopsis of the article and how the information may be incorporated into your paper ( i.e. why is this article important).   These sources should be journal articles.   You will not find econometric studies sufficient for this paper on the web, however some journals do have articles on the web.   Be careful if you use these articles, the tables are not usually included.  

3. ABSTRACT                                                                                                Due February 9 th

In approximately one page, you should write a concise statement of the problem you are researching.   Using your annotated bibliography, comment on the previous research that has examined this problem.   List the source(s) of data that you will be analyzing in your paper.

4. MODEL                                                                                                      DUE February 16 th

Please discuss your theoretical model.   Include a description of your dependent and independent variables as well as a discussion of the expected sign for each of your variables. Included in this discussion should be what previous research has shown concerning your variable. Also, indicate the data sources for each of the variables.

5. DATA AND DESCRIPTIVE STATISTICS                                            DUE March 7 th     

This write-up should include a discussion of all data sources that you have used for your project.   Furthermore, you should also discuss any possible econometric problems with your data.   Then, using this data, provide descriptive statistics for the variables in your model using both Excel and Stata .   Included in this should be a table that provides the variables, their definition, mean and standard deviation.  

6. INDIVIDUAL MEETINGS                                                                   Week March 27 th –31 st  

I will meet with you individually during this week.   At this meeting you should bring results that you have from regressions that you have run, a rough outline/draft of the introduction, literature review, model and descriptive statistics section of your paper and any questions that you may have regarding your paper.  

7. ROUGH DRAFT                                                                                        Due April 6 th  

This should be the complete draft of your paper.   I will be grading these papers as if they were the final draft.   The more work you put into this draft of the paper, the better your grade will be on the final draft as I will be able to provide more comments and suggestions.

8. FINAL DRAFT                                                                                           Due April 27 th   

The final draft of your paper will be due on April 22 nd .   You will need to turn in your rough draft with your final version of the paper.   Moreover, I will need 2 copies of your final draft, one for me to grade the other to be part of the department’s writing assessment.

Econometrics Research Topics and Term Paper Ideas

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• U.S. Economy
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One of the most difficult things about being an undergraduate student  in economics is that most schools require that students write an econometrics paper at some point in their studies. Econometrics is essentially the application of statistical and mathematical theories and perhaps some computer science to economic data. The objective is to develop empirical evidence for economics hypotheses and to predict future trends by testing economics models through statistical trials.

Econometrics assists economists in analyzing large sets of data to unveil meaningful relationships among them. For instance, an econometrics scholar might attempt to find statistical evidence for answers to real-world economics questions like, "does increased education spending lead to higher economic growth?" with the help of econometrics methods.

The Difficulty Behind Econometrics Projects

While certainly important to the subject of economics, many students (and particularly those who do not particularly enjoy statistics ) find econometrics a necessary evil in their education. So when the moment arrives to find an econometrics research topic for a university term paper or project, they are at a loss. In my time as an economics professor, I have seen students spend 90% of their time simply trying to come up with an econometrics research topic and then searching for the necessary data. But these steps need not be such a challenge.

Econometrics Research Topic Ideas

When it comes to your next econometrics project, I have you covered. I've come up with a few ideas for suitable undergraduate econometrics term papers and projects. All the data you will need to get started on your project is included, though you may choose to supplement with additional data. The data is available for download in Microsoft Excel format, but it can easily be converted to whatever format your course requires you to use.

Here are two econometrics research topic ideas to consider. Within these links are paper topic prompts, research resources, important questions to consider, and data sets to work with.

Okun's Law

Use your econometrics term paper to test Okun's Law in the United States. Okun's Law is named for American economist Arthur Melvin Okun, who was the first to propose the existence of the relationship back in 1962. The relationship described by Okun's Law is between that of a country's unemployment rate and that country's production or gross national product (GNP).

Spending on Imports and Disposable Income

Use your econometrics term paper as an opportunity to answer questions about American spending behaviors. As incomes rise, how do households spend their new wealth and disposable income? Do they spend it on imported goods or domestic goods? 

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Research Paper in Introductory Econometrics

Through this independent research project, students experience the process of doing real economics research using appropriate econometric methods.

Expand for more detail

Activity Classification and Connections to Related Resources Collapse

Grade level, learning goals.

Students will:

• Develop an understanding of how economists conduct applied research. This means more than simply learning the statistical methods. In order to use the methods appropriately, students must know the underlying theory as well as the existing literature on the issue.
• Develop important (marketable) computer skills. To handle the large data sets and complex econometric techniques several specialized software packages have emerged in the market. The program used in this class SAS. It is one of the most widely-used statistical programming languages in the world. While some work can be done with minimal knowledge of SAS coding, it is important for students to learn the basics of SAS syntax and logic to be an efficient econometrician.
• Develop the ability to critically evaluate others' research.
• Develop written and oral communication skills.

Context for Use

Description and teaching materials, teaching notes and tips.

The key is scaffolding the 6-week process so that students end with an econometrically rigorous and (relatively) complete paper. Of course, there is no way these kinds of papers can meet the level of thoroughness that you would expect out of a semester- or year-long independent research project. The instructor has to make deliberate decisions on where students should and should not devote their scarce time.

One area I sacrifice in is the literature. This is not a thesis and does not require a full-blown literature review. Having said that, students do need to have read at least a handful (6-8 is a reasonable expectation) of papers on the topic. I should note that many of these papers are related to other term papers students have written or are writing in their upper-level electives. For example, a student writing a paper about the literature on the gender-wage gap for a labor class will already have an extensive knowledge of the literature. The implication here is that there are spillovers from other classes that make use of to make this project successful. What they will not have done in that class, however, is to have done a full-blown, rigorous econometric study. Many of the papers I see in econometrics are like this.

The most challenging part is to get them to develop enough of a theory so that they can make the appropriate econometric decisions. For example, if they are looking at the price of beach-front housing on the coast, they need to understand and explain the appropriateness of the Hedonic model and its assumptions in the context of this market to have addressed the question of simultaneity. Otherwise, students are doing little more than an "applied regression" paper (a statistics project vs. an econometrics project).

Furthermore, since the papers are individualized, each topic and dataset will present its own unique set of econometric challenges. These include (1) multicollinearity, (2) incorrect functional form, (3) heteroscedasticity, (4) autocorrelation, (5) omitted variables, (6) measurement error and (7) simultaneity. Students are expected to address the relevant problems in a satisfactory way. The challenge is to get them to think about their data and theoretical problems early on so that it is not merely an exercise in data-mining.

To help with that, I have developed a series of short homework assignments to (1) keep them on task and (2) lead them to address the requisite issues that are addressed in the course:

• After their proposals are approved, I require a 2-page written summary where they discuss each independent variable theoretically. They explain from theory the effects it should have on the dependent variable and why. In addition, I ask them to pay close attention to two things: (1) whether the variable is endogenous and why (or, if it is exogenous they must justify that); (2) whether the theoretical relationship is linear or non-linear. I also ask them to sketch the XY scatter plot from a theoretical point of view (remember: they have not collected the data yet).
• Following that, their data are due. They are required to come to class with the data imported into SAS. I check their data one-by-one and we discuss issues of dummy variables, transformations, etc. The data are generally due 10-14 days after the proposal. This leaves 4 weeks in the semester for them to complete the econometric work and write the paper.

The final challenge has to do with the timing of content. As they are doing their papers over the last 4 weeks, we are covering topics such as limited dependent variables and panel data. I end the delivery of new content the before Thanksgiving, leaving 2 weeks of class time for them to work on their projects in class and have an in-class final exam (I use the final exam period for presentations). This timing means that students doing topics using, say, logistic regression, do not have that knowledge until 2 weeks left in class. Thus, I have all students begin with benchmark OLS regression model. A lot of diagnostics can be done at this stage, even if OLS is inefficient due to the non-linearities. For example, multicollinearity can be dealt with in OLS.

Since this is an econometrics class, assessment of the papers are biased towards the course objectives. As with all papers, of course, I do expect them to be well-written and complete. But those criteria are treated more as "minus" if they are not up-to-par rather than something that will make the difference between, say and A and a B. Thus, grades are determined by:

• whether the economic theory and the specification of the benchmark econometric model are consistent;
• the extent to which the student has correctly diagnosed the relevant econometric problems;
• the extent to which the student has dealt with the econometric problems in an appropriate and convincing way;
• the extent to which the paper is well-written and complete (e.g., is there a reasonable introduction with a clearly defined thesis? has the student done a reasonable amount of literature review for a semester project? has the student written a reflective discussion of the results?)

References and Resources

See more Undergraduate Research »

The Young Economist’s Short Guide to Writing Economic Research

Attributes of writing economics.

• The discourse is often mathematical, with lots of formulas, lemmas, and proofs.
• Writing styles vary widely. Some authors are very dry and technical while a few are quite eloquent.

Economics writing is different from many other types of writing. It is essentially technical, and the primary goal is to achieve clarity. A clear presentation will allow the strength of your underlying analysis and the quality of your research to shine through.

Unlike prose writing in other disciplines, economics research takes time. Successful papers are not cranked out the night before a due date.

General Guidelines for Quality Research

Getting started.

The hardest part of any writing assignment is starting. Economics research usually begins with a strong understanding of literature, and papers require a section that summarizes and applies previous literature to what the paper at hand. This is the best way to start.

Your writing will demonstrate that you understand the findings that relate to the topic.

Economists use the first few paragraphs to set up research questions and the model and data they use to think about it. Sure, it can be dry, but this format ensures the write and reader have strong grasp on the subject and structure of the work that follows.

Clear and Concise Work

Clarity is hard to achieve, but revising and reworking a paper ensures it is easy to read

• Organize your ideas into an argument with the help of an outline.
• Define the important terms you will use
• State your hypothesis and proceed deductively to reach your conclusions
• Avoid excess verbiage
• Edit yourself, remove what is not needed, and keep revising until you get down to a simple, efficient way of communicating
• Use the active voice
• Put statements in positive form
• Omit needless words (concise writing is clear writing)
• In summaries, generally stick to one tense

Time Management

Poor time management can wreck the best-planned papers. Deadlines are key to successful research papers.

• Start the project by finding your topic
• Begin your research
• Start and outline
• Write a draft
• Revise and polish

The Language of Economic Analysis

Economic theory has become very mathematical. Most PhD students are mathematicians, not simply economics majors. This means most quality economic research requires a strong use of mathematical language. Economic analysis is characterized by the use of models, simplified representations of how economic phenomena work. A model’s predictions about the future or the past are essentially empirical hypotheses. Since economics is not easily tested in controlled experiments, research requires data from the real world (census reports, balance sheets), and statistical methods (regressions and econometrics) to test the predictive power of models and hypotheses based on those models.

The Writing Process

Finding a topic.

There are a million ways to find a topic. It may be that you are writing for a specific subfield of economics, so topics are limited and thus easier to pick. However, must research starts organically, from passive reading or striking news articles. Make sure to find something that interests you. Be sure to find a niche and make a contribution to the subfield.

You will also need a project that can be done within the parameters of the assignment (length, due date, access to research materials). A profoundly interesting topic may not be manageable given the time and other constraints you face. The key is to just be practical.

Be sure to start your research as soon as possible. Your topic will evolve along the way, and the question you begin with may become less interesting as new information draws you in other directions. It is perfectly fine to shape your topic based on available data, but don’t get caught up in endlessly revising topics.

Finding and Using Sources

There are two types of economic sources: empirical data (information that is or can be easily translated into numerical form), and academic literature (books and articles that help you organize your ideas).

Economic data is compiled into a number of useful secondary sources:

• Economic Report of the President
• Statistical Abstract of the United States
• National Longitudinal Survey
• Census data
• Academic journals

The Outline

A good outline acts as an agenda for the things you want to accomplish:

• Introduction: Pose an interesting question or problem
• Literature Review: Survey the literature on your topic
• Methods/Data: Formulate your hypothesis and describe your data
• Results: Present your results with the help of graphs and charts
• Discussion: Critique your method and/or discuss any policy implications
• Conclusions: Summarize what you have done; pose questions for further research

Writing a Literature Review

The literature review demonstrates your familiarity with scholarly work on your topic and lays the foundations for your paper. The particular issues you intent to raise, the terms you will employ, and the approach you will take should be defined with reference to previous scholarly works.

Presenting a Hypothesis

Formulate a question, problem or conjecture, and describe the approach you will take to answer, solve, or test it. In presenting your hypothesis, you need to discuss the data set you are using and the type of regression you will run. You should say where you found the data, and use a table, graph, or simple statistics to summarize them. In term papers, it may not be possible to reach conclusive results. Don’t be afraid to state this clearly and accurately. It is okay to have an inconclusive paper, but it is not okay to make overly broad and unsupported statements.

Presenting Results

There are essentially two decisions to make: (1) How many empirical results should be presented, and (2) How should these results be described in the text?

• Focus only on what is important and be as clear as possible. Both smart and dumb readers will appreciate you pointing things out directly and clearly.
• Less is usually more: Reporting a small group of relevant results is better than covering every possible statistical analysis that could be made on the data.
• Clearly and precisely describe your tables, graphs, and figures in the text of your results section. The first and last sentence in a paragraph describing a result should be “big picture” statements, describing how the results in the table, graph or figure fit into the overall theme of the paper.

Discussing Results

The key to discussing results is to stay clear of making value judgments, and rely instead on economic facts and analyses. It is not the job of an economist to draw policy conclusions, even if the research supports strong evidence in a particular direction.

Referencing Sources

As with any research paper, source referencing depends on the will of a professor a discourse community. However, economists generally use soft references in the literature review section and then cite sources in conventional formats at the end of papers.

This guide was made possible by the excellent work of Robert Neugeboren and Mireille Jacobson of Harvard University and Paul Dudenhefer of Duke University.

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Econometrics Paper

Multivariate kernel regression in vector and product metric spaces.

Marcia M Schafgans and Victoria Zinde-Walsh

Nonparametric kernel regression is widely used in econometrics and has been applied to models with cross-sectional, time series and panel data. As functional data analysis is gaining attention, our analysis extends to th... Read more...

Keywords: nadaraya-watson estimator ; singular distribution ; multivariate functional regression ; small cube probability

Empirical likelihood for manifolds

Daisuke Kurisu and Taisuke Otsu

There has been growing interest in statistical analysis on random objects taking values in a non-Euclidean metric space. One important class of such objects consists of data on manifolds. This article is concerned with i... Read more...

6 February 2024

Keywords: riemannian manifold ; empirical likelihood

Nonparametric causal inference with functional covariates

Daisuke Kurisu, Taisuke Otsu and Mengshan Xu

Functional data and their analysis have become increasingly popular in various fields of data sciences. This paper considers estimation and inference of the average treatment effect under unconfoundedness when the covari... Read more...

25 October 2023

Keywords: causal inference ; functional data

Inference in the presence of unknown rates

Hao Dong, Taisuke Otsu and Luke Taylor

The convergence rate of an estimator can vary when applied to datasets from differ- ent populations. As the population is unknown in practice, so is the corresponding convergence rate. In this paper, we introduce a metho... Read more...

26 September 2023

Keywords: subsampling ; convergence rate ; measurement error

Empirical likelihood for network data

Yukitoshi Matsushita and Taisuke Otsu

This article develops a concept of nonparametric likelihood for network data based on network moments, and proposes general inference methods by adapting the theory of jackknife empirical likelihood. Our methodology can ... Read more...

7 August 2023

Keywords: network data ; empirical likelihood ; jackknife

Model averaging for global Fréchet regression

Petersen and Müller (2019) generalized the notion of regression analysis to non-Euclidean response objects. Meanwhile, in the conventional regression analysis, model averaging has long history and is widely applied in st... Read more...

21 July 2023

Keywords: non-euclidean data ; fréchet regression ; model averaging ; cross validation

Regression adjustment in randomized controlled trials with many covariates

Harold D Chiang, Yukitoshi Matsushita and Taisuke Otsu

This paper is concerned with estimation and inference on average treatment effects in randomized controlled trials when researchers observe potentially many covariates. By em- ploying Neyman’s (1923) finite population pe... Read more...

9 February 2023

Keywords: randomized controlled trials ; regression adjustment ; many covariates

Regression discontinuity design with potentially many covariates

Yoichi Arai, Taisuke Otsu and Myung Hwan Seo

This paper studies the case of possibly high-dimensional covariates in the regression discontinuity design (RDD) analysis. In particular, we propose estimation and inference methods for the RDD models with covariate sele... Read more...

31 October 2022

Keywords: regression discontinuity design ; covariate selection ; lasso

GLS under monotone heteroskedasticity

Yoichi Arai, Taisuke Otsu and Mengshan Xu

The generalized least square (GLS) is one of the most basic tools in regression analyses. A major issue in implementing the GLS is estimation of the conditional variance function of the error term, which typically requir... Read more...

27 October 2022

Keywords: generalized least squares ; monotonicity ; isotonic regression

Conditional likelihood ratio test with many weak instruments

Sreevidya Ayyar, Yukitoshi Matsushita and Taisuke Otsu

This paper extends validity of the conditional likelihood ratio (CLR) test developed by Moreira (2003) to instrumental variable regression models with unknown error variance and many weak instruments. In this setting, we... Read more...

11 October 2022

Keywords: many weak instruments ; conditional likelihood ratio test

Isotonic propensity score matching

Taisuke Otsu and Mengshan Xu

We propose a one-to-many matching estimator of the average treatment effect based on propensity scores estimated by isotonic regression. The method relies on the monotonicity assumption on the propensity score function, ... Read more...

19 July 2022

Keywords: matching ; propensity score ; isotonic regression

Minimax Risk in Estimating Kink Threshold and Testing

Javier Hidalgo, Heejun Lee, Jungyoon Lee and Myung Hwan Seo

We derive a risk lower bound in estimating the threshold parameter without knowing whether the threshold regression model is continuous or not. The bound goes to zero as the sample size n grows only at the cube root ra... Read more...

3 March 2022

Keywords: continuity test ; kink ; risk lower bound ; unknown threshold

Nonparametric prediction with spatial data

Abhimanyu Gupta and Javier Hidalgo

We describe a (nonparametric) prediction algorithm for spatial data, based on a canonical factorization of the spectral density function. We provide theoretical results showing that the predictor has desirable asymptot... Read more...

Keywords: lattice data ; unilateral models ; canonical factorization ; spectral density ; nonparametric prediction

Bandwidth selection for nonparametric regression with errors-in-variables

We propose two novel bandwidth selection procedures for the nonparametric regression model with classical measurement error in the regressors. Each method is based on evaluating the prediction errors of the regression us... Read more...

25 January 2022

Keywords: bandwidth selection ; measurement error ; bootstrap

Estimating density ratio of marginals to joint: Applications to causal inference

Yukitoshi Matsushita, Taisuke Otsu and Keisuke Takahata

In various fields of data science, researchers often face problems of estimating the ratios of two probability densities. Particularly in the context of causal inference, the product of marginals for a treatment variable... Read more...

7 January 2022

Keywords: density ratio ; causal inference ; nonparametric estimation

Equilibrium multiplicity in dynamic games: testing and estimation

Taisuke Otsu and Martin Pesendorfer

This paper surveys the recent literature on dynamic games estimation when there is a concern of equilibrium multiplicity. We focus on the questions of testing for equilibrium multiplicity and estimation in the presence o... Read more...

26 October 2021

Keywords: dynamic markov game ; multiplicity of equilibria

Multiway empirical likelihood

his paper develops a general methodology to conduct statistical inference for observations indexed by multiple sets of entities. We propose a novel multiway empirical likeli- hood statistic that converges to a chi-square... Read more...

19 October 2021

Keywords: multiway data ; empirical likelihood ; bipartite network

Nonparametric inference for extremal conditional quantiles

This paper studies asymptotic properties of the local linear quantile estimator under the extremal order quantile asymptotics, and develops a practical inference method for conditional quantiles in extreme tail areas. By... Read more...

14 September 2021

Keywords: quantile regression ; extreme value theory ; point process ; subsampling

On linearization of nonparametric deconvolution estimators for repeated measurements model

By utilizing intermediate Gaussian approximations, this paper establishes asymptotic linear representations of nonparametric deconvolution estimators for the classical measurement error model with repeated measurements. ... Read more...

19 July 2021

Keywords: measurement error ; deconvolution ; confidence band

Reweighted nonparametric likelihood inference for linear functionals

Karun Adusumilli, Taisuke Otsu and Chen Qiu

This paper is concerned with inference on finite dimensional parameters in semiparametric moment condition models, where the moment functionals are linear with respect to unknown nuisance functions. By exploiting this li... Read more...

1 December 2020

Keywords: nonparametric likelihood ; linear functional ; balancing weights

Jackknife Lagrange multiplier test with many weak instruments

This paper proposes a jackknife Lagrange multiplier (JLM) test for instrumental variable regression models, which is robust to (i) many instruments, where the number of instruments may increase proportionally with the sa... Read more...

5 August 2020

Keywords: many instruments ; weak instruments ; lagrange multiplier test ; jackknife

Second-order refinements for t-ratios with many instruments

This paper studies second-order properties of the many instruments robust t-ratios based on the limited information maximum likelihood and Fuller estimators for instrumental variable regression models under the many inst... Read more...

12 May 2020

Keywords: simultaneous equation ; many instrumental variables ; higher order expansion

Estimation of (static or dynamic) games under equilibrium multiplicity

Taisuke Otsu, Martin Pesendorfer, Yuya Sasaki and Yuya Takahashi

We propose a multiplicity-robust estimation method for (static or dynamic) games. The method allows for distinct behaviors and strategies across markets by treating market specific behaviors as correlated latent variable... Read more...

20 January 2020

Switching Regressions with Imperfect Regime Classification Information: Theory and Applications

V A Hajivassiliou

Read more...

8 January 2020

Keywords: switching regressions models ; measurement errors ; trigger-price mechanisms ; price-&#133 ; xing

Estimation and Specification Testing of Panel Data Models with Non-Ignorable Persistent Heterogeneity, Contemporaneous and Intertemporal Simultaneity, and Observable and Unobservable Dynamics

This paper proposes efficient estimation methods for panel data limited dependent variables (LDV) models possessing a variety of complications: non-ignorable persistent heterogeneity; contemporaneous and intertemporal en... Read more...

3 December 2019

Nonparametric intermediate order regression quantiles

Hidehiko Ichimura, Taisuke Otsu and Joseph Altonji

This paper studies nonparametric estimation of d-dimensional conditional quantile functions and their derivatives in the tails. We investigate asymptotic properties of the local and global nonparametric quantile regressi... Read more...

26 November 2019

Keywords: quantile regression ; local polynomial regression: extremes

Estimation of Varying Coefficient Models with Measurement Error

We propose a semiparametric estimator for varying coe&#64259;cient models when the regressors in the nonparametric component are measured with error. Varying coe&#64259;cient models are an extension of other popular semi... Read more...

Novel Approaches to Coherency Conditions in Dynamic LDV Models: Quantifying Financing Constraints and a Firm's Decision and Ability to Innovate

V A Hajivassiliou, Frédérique Savignac and Frédérique Savignac

We develop novel methods for establishing coherency conditions in Static and Dynamic Limited Dependent Variables (LDV) Models. We propose estimation strategies based on Conditional Maximum Likelihood Estimation for simul... Read more...

5 November 2019

Keywords: financing constraints ; innovation ; dynamic limited dependent variable models ; joint bivariate probit model ; econometric coherency conditions ; state dependence

Jackknife, small bandwidth and high-dimensional asymptotics

This paper sheds light on problems of statistical inference under alternative or nonstandard asymptotic frameworks from the perspective of jackknife empirical likelihood (JEL). Examples include small bandwidth asymptotic... Read more...

22 July 2019

Keywords: jackknife ; empirical likelihood ; nonstandard asymptotics

On the uniform convergence of deconvolution estimators from repeated measurements

This paper studies the uniform convergence rates of Li and Vuong's (1998) nonparametric deconvolution estimator and its regularized version by Comte and Kappus (2015) for the classical measurement error model, where repe... Read more...

Keywords: measurement error ; deconvolution ; uniform convergence

Score estimation of monotone partially linear index model

Mengshan Xu and Taisuke Otsu

17 May 2019

Average derivative estimation under measurement error

Causal inference on regression discontinuity designs by high-dimensional methods.

7 January 2019

Nonparametric Estimation of Additive Model with Errors-in-Variables

Hao Dong and Taisuke Otsu

In estimation of nonparametric additive models, conventional methods, such as backfitting and series approximation, cannot be applied when measurement errors are present in covariates. We propose an estimator for such ... Read more...

27 November 2018

Keywords: additive model ; measurement error ; deconvolution

Likelihood ratio inference for missing data models

Karun Adusumilli and Taisuke Otsu

Missing or incomplete outcome data is a ubiquitous problem in biomedical and social sciences. Under the missing at random setup, inverse probability weighting is widely applied to estimate and make inference on the popul... Read more...

30 October 2018

Keywords: missing data ; empirical balancing ; treatment effect ; nonparametric likelihood

Likelihood corrections for two-way models

Koen Jochmans and Taisuke Otsu

The use of two-way fixed-effect models is widespread. The presence of incidental parameter bias, however, invalidates statistical inference based on the likelihood. In this paper we consider modifications to the (profile... Read more...

19 February 2018

Keywords: asymptotic bias ; bias correction ; fixed effects ; information bias ; modified profile likelihood ; panel data ; mcmc ; penalization ; rectangular-array asymptotics

Inference Without Smoothing for Large Panels with Cross- Sectional and Temporal Dependence

Javier Hidalgo and Marcia M Schafgans

This paper addresses inference in large panel data models in the presence of both cross-sectional and temporal dependence of unknown form. We are interested in making inferences without relying on the choice of any smoo... Read more...

20 February 2018

Keywords: large panel data models ; cross-sectional strong-dependence ; central limit theorems ; clustering ; discrete fourier transformation ; nonparametric bootstrap algorithms

Adaptive Inference on Pure Spatial Models

Jungyoon Lee and Peter M Robinson

We consider adaptive tests and estimates which are asymptotically efficient in the presence of unknown, nonparametric, distributional form in pure spatial models. A novel adaptive Lagrange Multiplier testing procedure f... Read more...

26 January 2018

Keywords: efficient test ; adaptive estimation ; spatial models

Information theoretic approach to high dimensional multiplicative models: Stochastic discount factor and treatment effect

Taisuke Otsu and Chen Qiu

This paper is concerned with estimation of functionals of a latent weight function that satisfies possibly high dimensional multiplicative moment conditions. Main examples are missing data problems, treatment effects, an... Read more...

4 January 2018

Keywords: stochastic discount factor ; treatment effect ; information theory ; high dimension

Inference on distribution functions under measurement error

Karun Adusumilli, Taisuke Otsu and Yoon-Jae Whang

13 November 2017

Keywords: measurement error ; confidence band ; stochastic dominance

Relative error accurate statistic based on nonparametric likelihood

Lorenzo Camponovo, Yukitoshi Matsushita and Taisuke Otsu

This paper develops a new test statistic for parameters defined by moment conditions that exhibits desirable relative error properties for the approximation of tail area probabilities. Our statistic, called the tilted ex... Read more...

Keywords: nonparametric likelihood ; saddlepoint ; moment condition model

Likelihood inference on semiparametric models: Average derivative and treatment effect

In the past few decades, much progress has been made in semiparametric modeling and estimation methods for econometric analysis. This paper is concerned with inference (i.e., confidence intervals and hypothesis testing) ... Read more...

27 June 2017

Keywords: semiparametric ; jackknife ; empirical likelihood

Empirical likelihood for high frequency data

With increasing availability of high frequency financial data as a background, various volatility measures and related statistical theory are developed in the recent literature. This paper introduces the method of empiri... Read more...

21 February 2017

Keywords: high frequency data ; volatility ; empirical likelihood

Robust Inference and Testing of Continuity in Threshold Regression Models

Javier Hidalgo, Jungyoon Lee and Myung Hwan Seo

This paper is concerned with inference in regression models with either a kink or a jump at an unknown threshold, particularly when we do not know whether the kink or jump is the true specification. One of our main resu... Read more...

13 February 2017

Local M-estimation with discontinuous criterion for dependent and limited observations

Myung Hwan Seo and Taisuke Otsu

This paper examines asymptotic properties of local M-estimators under three sets of high-level conditions. These conditions are sufficiently general to cover the minimum volume predictive region, conditional maximum scor... Read more...

17 October 2016

Keywords: cube root asymptotics ; maximal inequality ; mixing process ; partial identification ; parameter-dependent localization

Likelihood inference on semiparametric models with generated regressors

Hahn and Ridder (2013) formulated influence functions of semiparametric three step estimators where generated regressors are computed in the first step. This class of estimators covers several important examples for empi... Read more...

5 September 2016

Keywords: generated regressor ; empirical likelihood

Specification testing for errors-in-variables models

Taisuke Otsu and Luke Taylor

This paper considers specification testing for regression models with errors-in-variables and proposes a test statistic comparing the distance between the parametric and nonparametric fits based on deconvolution techniqu... Read more...

15 August 2016

Keywords: specification test ; measurement errors ; deconvolution

Nonparametric instrumental regression with errors in variables

This paper considers nonparametric instrumental variable regression when the endogenous variable is contaminated with classical measurement error. Existing methods are inconsistent in the presence of measurement error. W... Read more...

21 July 2015

Keywords: nonparametric instrumental variable regression ; measurement error ; inverse problem ; deconvolution ; measurement error

Testing for Breaks in Regression Models with Dependent Data

Violetta Dalla and Javier Hidalgo

4 June 2015

Keywords: nonparametric regression ; breaks ; smoothness ; strong dependence ; extreme-values distribution ; frequency domain bootstrap algorithms.

Inference and Testing Breaks in Large Dynamic Panels with Strong Cross Sectional Dependence

This paper is concerned with various issues related to inference in large dynamic panel data models (where both n and T increase without bound) in the presence of, possibly, strong cross-sectional dependence. Our first a... Read more...

1 April 2015

Keywords: large panel data ; dynamic models ; cross-sectional strong-dependence ; central limit theorems ; homogeneity ; bootstrap algorithms

Pooling data across markets in dynamic Markov games

Taisuke Otsu, Martin Pesendorfer and Yuya Takahashi

This paper proposes several statistical tests for finite state Markov games to examine the null hypothesis that data from distinct markets can be pooled. We formulate tests of (i) the conditional choice and state transit... Read more...

16 March 2015

Keywords: dynamic markov game ; poolability ; multiplicity of equilibria ; hypothesis testing

Nonparametric likelihood for volatility under high frequency data

We propose a nonparametric likelihood inference method for the integrated volatility under high frequency financial data. The nonparametric likelihood statistic, which contains the conventional statistics such as empiric... Read more...

15 January 2015

Keywords: nonparametric likelihood ; volatility ; high frequency data

Bootstrap inference of matching estimators for average treatment effects

Taisuke Otsu and Yoshiyasu Rai

Abadie and Imbens (2008) showed that the standard naive bootstrap is inconsistent to estimate the distribution of the matching estimator for treatment effects with a fixed number of matches. This article proposes an asym... Read more...

13 January 2015

Keywords: treatment effect ; matching ; bootstrap

Robust estimation of moment condition models with weakly dependent data

Kirill Evdokimov, Yuichi Kitamura and Taisuke Otsu

This paper considers robust estimation of moment condition models with time series data. Researchers frequently use moment condition models in dynamic econometric analysis. These models are particularly useful when one w... Read more...

5 December 2014

Keywords: blocking ; generalized empirical likelihood ; hellinger distance ; robustness ; efficient estimation ; mixing

Regularization for Spatial Panel Time Series Using the Adaptive LASSO

Clifford Lam and Pedro Souza

This paper proposes a model for estimating the underlying cross-sectional dependence structure of a large panel of time series. Technical difficulties meant such a structure is usually assumed before further analysis. We... Read more...

25 November 2014

Keywords: spatial econometrics ; adaptive lasso ; sign consistency ; asymptotic normality ; non-asymptotic oracle inequalities ; spatial weight matrices

Dynamic Panels with Threshold Effect and Endogeneity

Myung Hwan Seo and Yongcheol Shin

This paper addresses an important and challenging issue as how best to model nonlinear asymmetric dynamics and cross-sectional heterogeneity, simultaneously, in the dynamic threshold panel data framework, in which both t... Read more...

1 September 2014

Keywords: dynamic panel threshold models ; endogenous threshold effects and regressors ; fd-gmm and fd-2sls estimation ; linearity test ; exogeneity test ; investment and dividend smoothing.

A Cusum Test of Common Trends in Large Heterogeneous Panels

Javier Hidalgo and Jungyoon Lee

This paper examines a nonparametric CUSUM-type test for common trends in large panel data sets with individual fixed effects. We consider, as in Zhang, Su and Phillips (2012), a partial linear regression model with unkno... Read more...

Keywords: common trends ; large data set ; partial linear models ; bootstrap algorithms

Estimation of Nonseparable Models with Censored Dependent Variables and Endogenous Regressors.

In this paper we develop a nonparametric estimator for the local average response of a censored dependent variable to endogenous regressors in a nonseparable model where the unobservable error term is not restricted to b... Read more...

6 August 2014

Empirical Likelihood for Random Sets

We extend the method of empirical likelihood to cover hypotheses involving the Aumann expectation of random sets. By exploiting the properties of random sets, we convert the testing problem into one involving a continuum... Read more...

25 June 2014

Empirical Likelihood for Regression Discontinuity Design

Taisuke Otsu, Ke-Li Xu and Yukitoshi Matsushita

7 February 2014

Robustness of bootstrap in instrumental variable regression

Lorenzo Camponovo and Taisuke Otsu

This paper studies robustness of bootstrap inference methods for instrumental variable (IV)regression models. We consider test statistics for parameter hypotheses based on the IV estimatorand generalized method of trimme... Read more...

21 January 2014

Keywords: bootstrap ; breakdown point ; instrumental variables

Asymptotics for maximum score method under general conditions

Abstract. Since Manski's (1975) seminal work, the maximum score method for discrete choice models has been applied to various econometric problems. Kim and Pollard (1990) established the cube root asymptotics for the max... Read more...

Revised August 2014

Keywords: maximum score ; cube root asymptotics ; set inference

Series Estimation under Cross-sectional Dependence

An asymptotic theory is developed for nonparametric and semiparametric series estimation under general cross-sectional dependence and heterogeneity. A uniform rate of consistency, asymptotic normality, and sufficient con... Read more...

25 November 2013

Keywords: series estimation ; nonparametric regression ; spatial data ; cross-sectional dependence ; uniform rate of consistency ; functional central limit the- orem ; data-driven studentization

Panel Nonparametric Regression with Fixed Effects

Nonparametric regression is developed for data with both a temporal and a cross-sectional dimension. The model includes additive, unknown, individual-specifi…c components and allows also for cross-sectional and temporal ... Read more...

Keywords: panel data ; nonparametric regression ; cross-sectional dependence ; generalized least squares ; optimal bandwidth

Non-Nested Testing of Spatial Correlation

Miguel A. Delgado and Peter M Robinson

We develop non-nested tests in a general spatial, spatio-temporal or panel data context. The spatial aspect can be interpreted quite generally, in either a geographical sense, or employing notions of economic distance, o... Read more...

Keywords: on-nested test ; spatial correlation ; pseudo maximum likelihood estimation

Efficient Inference on Fractionally Integrated Panel Data Models with Fixed Effects

Peter M Robinson and Carlos Velasco

A dynamic panel data model is considered that contains possibly stochastic individual components and a common fractional stochastic time trend. We propose four different ways of coping with the individual effects so as t... Read more...

20 November 2013

Keywords: panel data ; fractional time series ; estimation ; testing ; bias correction

Improved Lagrange Multiplier Tests in Spatial Autoregressions

Peter M Robinson and Francesca Rossi

For testing lack of correlation against spatial autoregressive alternatives, Lagrange multiplier tests enjoy their usual computational advantages, but the (x squared) first-order asymptotic approximation to critical valu... Read more...

Keywords: spatial autocorrelation ; lagrange multiplier test ; edgeworth expansion ; bootstrap ; finite-sample corrections.

Improved Tests for Spatial Correlation

We consider testing the null hypothesis of no spatial autocorrelation against the alternative of first order spatial autoregression. A Wald test statistic has good first order asymptotic properties, but these may not be ... Read more...

Keywords: spatial autocorrelation ; ordinary least squares ; hypothesis testing ; edgeworth expansion ; bootstrap.

Extremum Sieve Estimation in k-out-of-n Systems

Tatiana Komarova

The paper considers nonparametric estimation of absolutely continuous distribution functions of lifetimes of non-identical components in k-out-of-n systems from the observed “autopsy” data. In economics,ascending “button... Read more...

27 August 2013

Keywords: k-out-of-n systems ; competing risks ; sieve estimation ; bernstein polynomials

Testing for equality of an increasing number of spectral density functions

Javier Hidalgo, Pedro Souza and Pedro Souza

Nowadays it is very frequent that a practitioner faces the problem of modelling large data sets. Relevant examples include spatio-temporal or panel data models with large N and T. In these cases deciding a particular dyn... Read more...

1 July 2013

SPECIFICATION FOR LATTICE PROCESSES

Javier Hidalgo and Myung Hwan Seo

We consider an omnibus test for the correct speci…cation of the dynamics of a sequence fx (t)gt2Zd in a lattice. As it happens with causal models and d = 1, its asymptotic distribution is not pivotal and depends on the e... Read more...

15 May 2013

Keywords: specification test ; spatial processes ; lattice ; spectral domain ; cusum ; bootstrap.

Testing for Structural Stability in the Whole Sample

The paper examines a Lagrange Multiplier type test for the constancy of the parameter in general models with dependent data without imposing any arti…cial choice of the possible location of the break. In order to prove t... Read more...

1 September 2012

Keywords: structural stability ; gmm estimation ; strong approximation ; extreme value distribution.

ON TESTABILITY OF COMPLEMENTARITY IN MODELS WITH MULTIPLE EQUILIBRIA

This paper revisits testability of complementarity in economic models with multiple equilibria studied by Echenique and Komunjer (2009). We find that Echenique and Komunjer’s (2009) testable implications on extreme quant... Read more...

1 February 2013

Keywords: complementarity ; testability ; quantile.

Binary Choice Models with Discrete Regressors: Identification and Misspecification

In semiparametric binary response models, support conditions on the regressors are required to guarantee point identification of the parameter of interest. For example,one regressor is usually assumed to have continuous ... Read more...

Keywords: binary response models ; discrete regressors ; partial identification ; misspecification ; support vector machines

TESTING FOR STRUCTURAL STABILITY IN THE WHOLE SAMPLE

Javier Hidalgo and Myunghwan Seo

The paper examines a Lagrange Multiplier type test for the constancy of the parameter in general models with dependent data without imposing any artificial choice of the possible location of the break. In order to prove ... Read more...

1 October 2011

Keywords: structural stability. gmm estimation. strong approximation. extreme value distribution.

Adapting Kernel Estimation to Uncertain Smoothness

Yulia Kotlyarova, Marcia M Schafgans and Victoria Zinde-Walsh

For local and average kernel based estimators, smoothness conditions ensure that the kernel order determines the rate at which the bias of the estimator goes to zero and thus allows the econometrician to control the rate... Read more...

Keywords: nonparametric estimation ; kernel based estimator ; combined stimator ; variance bootstrap.

Inference on Power Law Spatial Trends (Running Title: Power Law Trends)

Peter M Robinson

Power law or generalized polynomial regressions with unknown real-valued exponents and coefficients, and weakly dependent errors, are considered for observations over time, space or space-time. Consistency and asymptotic... Read more...

Keywords: asymptotic normality ; consistency ; correlation ; generalized polynomial ; lattice ; power law.0út

Asymptotic Theory for Nonparametric Regression with Spatial Data

Nonparametric regression with spatial, or spatio-temporal, data is considered. The conditional mean of a dependent variable, given explanatory ones, is a nonparametric function, while the conditional covariance reflects ... Read more...

September 2010

Keywords: nonparametric regression ; spatial data ; weak dependence ; long range dependence ; heterogeneity ; consistency ; central limit theorem. ;

Statistical Inference on Regression with Spatial Dependence

Peter M Robinson and Supachoke Thawornkaiwong

Central limit theorems are developed for instrumental variables estimates of linear and semi-parametric partly linear regression models for spatial data. General forms of spatial dependence and heterogeneity in explanato... Read more...

Keywords: linear regression ; partly linear regression ; nonparametric regression ; spatial data ; instrumental variables ; asymptotic normality ; variance estimation

Nonparametric Trending Regression with Cross-Sectional Dependence

Panel data, whose series length T is large but whose cross-section size N need not be, are assumed to have a common time trend. The time trend is of unknown form, the model includes additive, unknown, individual-specific... Read more...

January 2010

Keywords: panel data ; nonparametric time trend ; cross-sectional dependence ; generalized least squares ; optimal bandwidthw

Quantile Uncorrelation and Instrumental Regression

Tatiana Komarova, Thomas Severini and Elie Tamer

We introduce a notion of median uncorrelation that is a natural extension of mean (linear) uncorrelation. A scalar random variable Y is median uncorrelated with a kdimensional random vector X if and only if the slope fro... Read more...

Semiparametric Estimation of Locally Stationary Diffusion Models

Bonsoo Koo and Oliver Linton

This paper proposes a class of locally stationary diffusion processes. The model has a time varying but locally linear drift and a volatility coefficient that is allowed to vary over time and space. We propose estimators... Read more...

August 2010

Keywords: diffusion processes ; local stationarity ; term structure dynamics ; density matching ; option pricing.

Semiparametric Estimation of Markov Decision Processeswith Continuous State Space

Sorawoot Srisuma and Oliver Linton

We propose a general two-step estimation method for the structural parameters of popular semiparametric Markovian discrete choice models that include a class of Markovian Games and allow for continuous observable state s... Read more...

Keywords: discrete markov decision models ; kernel smoothing ; markovian games ; semi-parametric estimation ; well-posed inverse problem.d

Loch Linear Fitting under Near Epoch Dependence: Uniform Consistency with Convergence Rate

Degui Li, Zudi Lu and Oliver Linton

Local linear fitting is a popular nonparametric method in nonlinear statistical and econometric modelling. Lu and Linton (2007) established the point wise asymptotic distribution (central limit theorem) for the local lin... Read more...

Keywords: local linear fitting ; near epoch dependence ; convergence rates ; uniform consistency.

Estimation of Structural Optimization Models: A Note on Identification

Sorawoot Srisuma

Bajari, Benkard and Levin (2007) propose an estimation methodology for a broad class of dynamic optimization problems. To carry out their procedure, one needs to select a set of alternative policy functions and compare t... Read more...

Keywords: consistency ; identification ; optimization models

Nonparametric Identification in Asymmetric Second-Price Auctions: A New Approach

This paper proposes an approach to proving nonparametric identification for distributions of bidders' values in asymmetric second-price auctions. I consider the case when bidders have independent private values and the o... Read more...

October 2009

Efficient Estimation of a Multivariate Multiplicative Volatility Model

Christian M. Hafner and Oliver Linton

We propose a multivariate generalization of the multiplicative volatility model of Engle and Rangel (2008), which has a nonparametric long run component and a unit multivariate GARCH short run dynamic component. We sugge... Read more...

Keywords: garch ; kernel estimation ; local stationarity ; semiparametric

ESTIMATION OF A SEMIPARAMETRIC IGARCH(1,1) MODEL

Woocheol Kim and Oliver Linton

We propose a semiparametric IGARCH model that allows for persistence in variance but also allows for more flexible functional form. We assume that the difference of the squared process is weakly stationary. We propose an... Read more...

Keywords: inverse problem ; instrumental variable ; igarch ; kernel estimation ; nonparametric regression

Nonparametric Regression with a Latent Time Series

Oliver Linton, Søren Feodor Nielsen and Jens Perch Nielsen

In this paper we investigate a class of semiparametric models for panel datasets where the cross-section and time dimensions are large. Our model contains a latent time series that is to be estimated and perhaps forecast... Read more...

Keywords: kernel estimation ; forecasting ; panel data ; unit roots

Optimal Smoothing for a Computationally and Statistically Efficient Single Index Estimator

Wolfgang Härdle, Oliver Linton and Yingcun Xia

In semiparametric models it is a common approach to under-smooth the nonparametric functions in order that estimators of the finite dimensional parameters can achieve root-n consistency. The requirement of under-smoothin... Read more...

Keywords: ade ; asymptotics ; bandwidth ; mave method ; semiparametric efficiency.

An Alternative Way of Computing Efficient Instrumental Variable Estimators

Xiaohong Chen, David T. Jacho-Chávez and Oliver Linton

A new way of constructing efficient semiparametric instrumental variable estimators is proposed. The method involves the combination of a large number of possibly inefficient estimators rather than combining the instrume... Read more...

Keywords: instrumental variables ; minimum distance ; semiparametric efficiency ; two-stage least squares

Uniform Bahadur Representation for Local Polynomial Estimates of M-Regression and Its Application to The Additive Model

Efang Kong, Oliver Linton and Yingcun Xia

We use local polynomial fitting to estimate the nonparametric M-regression function for strongly mixing stationary processes {(Y_i,?X_i ) } . We establish a strong uniform consistency rate for the Bahadur representation ... Read more...

January 2009

Nonparametric Estimation of a Polarization Measure

Gordon Anderson, Oliver Linton and Yoon-Jae Whang

This paper develops methodology for nonparametric estimation of a polarization measure due to Anderson (2004) and Anderson, Ge, and Leo (2006) based on kernel estimation techniques. We give the asymptotic distribution th... Read more...

Keywords: kernel estimation ; inequality ; overlap coefficient ; poissonization

Large-Sample Inference on Spatial Dependence

We consider cross-sectional data that exhibit no spatial correla- tion, but are feared to be spatially dependent. We demonstrate that a spatial version of the stochastic volatility model of financial econometrics, entail... Read more...

Keywords: spatial dependence ; parameter estimation ; asymptotic theory ; independence testing.

Inference On Nonparametrically Trending Time Series With Fractional Errors

The central limit theorem for nonparametric kernel estimates of a smooth trend, with linearly-generated errors, indicates asymptotic independence and homoscedasticity across fixed points, irrespective of whether disturba... Read more...

Developments in the Analysis of Spatial Data

Disregarding spatial dependence can invalidate methods for analyzing cross-sectional and panel data. We discuss ongoing work on developing methods that allow for, test for, or estimate, spatial dependence. Much of the st... Read more...

Correlation Testing in Time Series, Spatial and Cross-Sectional Data

We provide a general class of tests for correlation in time series, spatial, spatiotemporal and cross-sectional data. We motivate our focus by reviewing how computational and theoretical difficulties of point estimation ... Read more...

Keywords: heteroscedasticity ; lagrange multiplier tests.

Smoothness Adaptive Average Derivative Estimation

Many important models, such as index models widely used in limited dependent variables, partial linear models and nonparametric demand studies utilize estimation of average derivatives (sometimes weighted) of the conditi... Read more...

August 2008

Keywords: nonparametric estimation ; density weighted average derivative estimator ; combined estimator.

Bootstrap Tests of Stochastic Dominance with Asymptotic Similarity on the Boundary

Oliver Linton, Kyungchul Song and Yoon-Jae Whang

We propose a new method of testing stochastic dominance which improves on existing tests based on bootstrap or subsampling. Our test requires estimation of the contact sets between the marginal distributions. Our tests h... Read more...

February 2008

Keywords: set estimation ; size of test ; unbiasedness ; similarity ; bootstrap ; subsampling.

Multiple Local Whittle Estimation in Stationary Systems

Moving from univariate to bivariate jointly dependent long memory time series introduces a phase parameter (?), at the frequency of principal interest, zero; for short memory series ? = 0 automatically. The latter case h... Read more...

October 2007

Keywords: long memory ; phase ; cointegration ; semiparametric estimation ; consistency ; asymptotic normality.

Efficient Estimation of a Semiparametric Characteristic-Based Factor Model of Security Returns

Gregory Connor, Matthias Hagmann and Oliver Linton

This paper develops a new estimation procedure for characteristic-based factor models of security returns. We treat the factor model as a weighted additive nonparametric regression model, with the factor returns serving ... Read more...

Keywords: additive models ; arbitrage pricing theory ; factor model ; fama-french ; kernel estimation ; nonparametric regression ; panel data.

Inference about Realized Volatility using Infill Subsampling

Ilze Kalnina and Oliver Linton

We investigate the use of subsampling for conducting inference about the quadratic variation of a discretely observed diffusion process under an infill asymptotic scheme. We show that the usual subsampling method of Poli... Read more...

September 2007

Keywords: realised volatility ; semimartingale ; subsampling ; infill asymptotic scheme

DIAGNOSTIC TESTING FOR COINTEGRATION

Peter Robinson

We develop a sequence of tests for specifying the cointegrating rank of, possibly fractional, multiple time series. Memory parameters of observables are treated as unknown, as are those of possible cointegrating errors. ... Read more...

Keywords: fractional cointegration ; diagnostic testing ; specification testing ; cointegrating rank ; semiparametric estimation.

ON DISCRETE SAMPLING OF TIME-VARYING CONTINUOUS-TIME SYSTEMS

We consider a multivariate continuous time process, generated by a system of linear stochastic differential equations, driven by white noise and involving coefficients that possibly vary over time. The process is observa... Read more...

Keywords: stochastic differential equations ; time-varying coefficients ; discrete sampling ; irregular sampling.

Fractional Cointegration In Stochastic Volatility Models

Afonso Gonçalves da Silva and Peter M Robinson

Asset returns are frequently assumed to be determined by one or more common factors. We consider a bivariate factor model, where the unobservable common factor and idiosyncratic errors are stationary and serially uncorre... Read more...

Keywords: fractional cointegration ; stochastic volatility ; narrow band least squares ; semiparametric analysis.

SPECIFICATION TESTING FOR REGRESSION MODELS WITH DEPENDENT DATA

Javier Hidalgo

We describe and examine a consistent test for the correct specification of a regression function with dependent data. The test is based on the supremum of the difference between the parametric and nonparametric estimates... Read more...

Keywords: functional specification. variable selection. nonparametric kernel regression. frequency domain bootstrap.

Estimation of Nonlinear Error Correction Models

Myung Hwan Seo

Asymptotic inference in nonlinear vector error correction models (VECM) that exhibit regime-specific short-run dynamics is nonstandard and complicated. This paper contributes the literature in several important ways. Fir... Read more...

Keywords: threshold cointegration ; smooth transition error correction ; least squares ; smoothed least squares ; consistency ; convergence rate.

SEMIPARAMETRIC ESTIMATION OF A BINARY RESPONSE MODEL WITH A CHANGE-POINT DUE TO A COVARIATE THRESHOLD

Sokbae Lee and Myunghwan Seo

This paper is concerned with semiparametric estimation of a threshold binary response model. The estimation method considered in the paper is semiparametric since the parameters for a regression function are finite-dimen... Read more...

February 2007

Keywords: binary response model ; maximum score estimation ; semiparametric estimation ; threshold regression ; nonlinear random utility models.

Efficient Estimation of the Semiparametric Spatial Autoregressive Model

Efficient semiparametric and parametric estimates are developed for a spatial autoregressive model, containing nonstochastic explanatory variables and innovations suspected to be non-normal. The main stress is on the cas... Read more...

Keywords: spatial autoregression ; efficient estimation ; adaptive estimation ; simultaneity bias. © the author. all rights reserved. short sections of text ; not to exceed two paragraphs ; may be quoted without explicit permission provided that full credit ; including © notice ; is given to the source.

Selectivity and the gender wage gap decomposition in the presence of a joint decision process

Marcia M Schafgans and Morton Stelcnery

In this paper we revisit the gender decomposition of wages in the presence of selection bias. We show that when labor market participation decisions of couples are not independent, the sample selection corrections used i... Read more...

December 2006

Keywords: sample selection model ; gender wage differences ; oaxaca wage decomposition ; ‘discrimination’.

Estimating Quadratic Variation Consistently in the Presence of Correlated Measurement Error

We propose an econometric model that captures the e¤ects of market microstructure on a latent price process. In particular, we allow for correlation between the measurement error and the return process and we allow the m... Read more...

October 2006

Keywords: endogenous noise ; market microstructure ; realised volatility ; semimartingale

Identification and Nonparametric Estimation of a Transformed Additively Separable Model

David T. Jacho-Chávez, Arthur Lewbel and Oliver Linton

Let r (x, z) be a function that, along with its derivatives, can be consistently estimated nonparametrically. This paper discusses identification and consistent estimation of the unknown functions H, M, G and F, where r ... Read more...

September 2006

Keywords: partly separable models ; nonparametric regression ; dimension reduction ; generalized homothetic function ; production function.

ESTIMATING FEATURES OF A DISTRIBUTION FROM BINOMIAL DATA

Arthur Lewbel, Oliver Linton and DL McFadden

A statistical problem that arises in several fields is that of estimating the features of an unknown distribution, which may be conditioned on covariates, using a sample of binomial observations on whether draws from thi... Read more...

Keywords: willingness to pay ; contingent valuation ; discrete choice ; bi-nomial response ; bioassay ; destructive duration testing ; semiparametric ; nonparametric ; latent variable models.

Semiparametric Estimation of a Characteristic-based Factor Model of Common Stock Returns

Gregory Connor and Oliver Linton

We introduce an alternative version of the Fama-French three-factor model of stock returns together with a new estimation methodology. We assume that the factor betas in the model are smooth nonlinear functions of observ... Read more...

Keywords: characteristic-based factor model ; arbitrage pricing theory ; kernel estimation ; nonparametric estimation.

Conditional-Sum-of-Squares Estimation of Models for Stationary Time Series with Long Memory

Employing recent results of Robinson (2005) we consider the asymptotic properties of conditional-sum-of-squares (CSS) estimates of parametric models for stationary time series with long memory. CSS estimation has been co... Read more...

Keywords: long memory ; conditional-sum-of-squares estimation ; central limit theorem ; almost sure convergence.

TESTING FOR STOCHASTIC MONOTONICITY

Sokbae Lee, Oliver Linton and Yoon-Jae Whang

We propose a test of the hypothesis of stochastic monotonicity. This hypothesis is of interest in many applications. Our test is based on the supremum of a rescaled U-statistic. We show that its asymptotic distribution i... Read more...

August 2006

Keywords: distribution function ; extreme value theory ; gaussian process ; monotonicity.

Nonparametric Transformation to White Noise

Oliver Linton and Enno Mammen

We consider a semiparametric distributed lag model in which the “news impact curve” m is nonparametric but the response is dynamic through some linear filters. A special case of this is a nonparametric regression with se... Read more...

Keywords: efficiency ; inverse problem ; kernel estimation ; nonparametric regression ; time series ; unit roots.

Semiparametric Estimation of Fractional Cointegration

Javier Hualde and Peter M Robinson

A semiparametric bivariate fractionally cointegrated system is considered, integration orders possibly being unknown and I (0) unobservable inputs having nonparametric spectral density. Two kinds of estimate of the coint... Read more...

Keywords: fractional cointegration ; semiparametric model ; unknown integration orders.

Finite Sample Performance in Cointegration Analysis of Nonlinear Time Series with Long Memory

Nonlinear functions of multivariate financial time series can exhibit long memory and fractional cointegration. However, tools for analysing these phenomena have principally been justified under assumptions that are inva... Read more...

Keywords: fractional cointegration ; memory estimation ; stochastic volatility.

Instrumental Variables Estimation of Stationary and Nonstationary Cointegrating Regressions

Peter M Robinson and M. Gerolimetto

Instrumental variables estimation is classically employed to avoid simultaneous equations bias in a stable environment. Here we use it to improve upon ordinary least squares estimation of cointegrating regressions betwee... Read more...

Keywords: cointegration ; instrumental variables estimation ; i(d) processes.

ROOT-N-CONSISTENT ESTIMATION OF WEAK FRACTIONAL COINTEGRATION

Empirical evidence has emerged of the possibility of fractional cointegration such that the gap, ß, between the integration order d of observable time series, and the integration order ? of cointegrating errors, is less ... Read more...

Keywords: fractional cointegration ; parametric estimation ; asymptotic normality.

Nonparametric Spectrum Estimation for Spatial Data

Smoothed nonparametric kernel spectral density estimates are considered for stationary data observed on a d-dimensional lattice. The implications for edge effect bias of the choice of kernel and bandwidth are considered.... Read more...

February 2006

Keywords: nonparametric spectrum estimation ; edge effect ; tapering.

Consistent estimation of the memory parameter for nonlinear time series

Violetta Dalla, Liudas Giraitis and Javier Hidalgo

For linear processes, semiparametric estimation of the memory parameter, based on the log-periodogram and local Whittle estimators, has been exhaustively examined and their properties are well established. However, excep... Read more...

January 2006

Keywords: long memory ; semiparametric estimation ; local whittle estimator.

A Smoothed Least Squares Estimator For Threshold Regression Models

Myunghwan Seo and Oliver Linton

We propose a smoothed least squares estimator of the parameters of a threshold regression model. Our model generalizes that considered in Hansen (2000) to allow the thresholding to depend on a linear index of observed re... Read more...

October 2005

Keywords: index model ; sample splitting ; segmented regression ; smoothing ; threshold estimation.

Pseudo-Maximum Likelihood Estimation of ARCH(8) Models

Peter M Robinson and Paolo Zaffaroni

Strong consistency and asymptotic normality of the Gaussian pseudo-maximum likelihood estimate of the parameters in a wide class of ARCH(8) processes are established. We require the ARCH weights to decay at least hyperbo... Read more...

Keywords: arch (8) ; pseudo-maximum likelihood estimation ; asymptotic inference

A method of moments estimator for semiparametric index models

Bas Donkers and Marcia M Schafgans

We propose an easy to use derivative based two-step estimation procedure for semi-parametric index models. In the first step various functionals involving the derivatives of the unknown function are estimated using nonpa... Read more...

Keywords: semiparametric estimation ; multiple index models ; average derivative functionals ; generalized methods of moments estimator ; rank testing

Modified Whittle Estimation of Multilateral Models on a Lattice

Peter M Robinson and J Vidal Sanz

In the estimation of parametric models for stationary spatial or spatio-temporal data on a d-dimensional lattice, for d >= 2, the achievement of asymptotic efficiency under Gaussianity, and asymptotic normality more gene... Read more...

Keywords: spatial data ; multilateral modelling ; whittle estimation ; edge effect ; consistent variance estimation

Modelling Memory of Economic and Financial Time Series

Much time series data are recorded on economic and financial variables. Statistical modelling of such data is now very well developed, and has applications in forecasting. We review a variety of statistical models from t... Read more...

Keywords: long memory ; short memory ; stochastic volatility

A Parametric Bootstrap Test for Cycles

The paper proposes a simple test for the hypothesis of strong cycles and as a by-product a test for weak dependence for linear processes. We show that the limit distribution of the test is the maximum of a (semi)Gaussian... Read more...

February 2005

Keywords: cyclical data ; strong and weak dependence ; spectral density functions ; whittle estimator ; bootstrap algorithms

Testable Implications of Forecast Optimality

Andrew J. Patton and Allan Timmermann

Evaluation of forecast optimality in economics and finance has almost exclusively been conducted on the assumption of mean squared error loss under which forecasts should be unbiased and forecast errors serially uncorrel... Read more...

January 2005

Keywords: forecast evaluation ; loss function ; rationality tests

Unit Root Test in a Threshold Autoregression: Asymptotic Theory and Residual-based Block Bootstrap

Myunghwan Seo

There is a growing literature on unit root testing in threshold autoregressive models. This paper makes two contributions to the literature. First, an asymptotic theory is developed for unit root testing in a threshold a... Read more...

Keywords: threshold autoregression ; unit root test ; threshold cointegration ; residual-based block bootstrap

The Bootstrap and the Edgeworth Correction for Semiparametric Averaged Derivatives

Yoshihiko Nishiyama and Peter M Robinson

In a number of semiparametric models, smoothing seems necessary in order to obtain estimates of the parametric component which are asymptotically normal and converge at parametric rate. However, smoothing can inflate the... Read more...

Keywords: bootstrap ; edgeworth correction ; semiparametric averaged derivatives

Distribution Free Goodness-of-Fit Tests for Linear Processes

Miguel A. Delgado, Javier Hidalgo and Carlos Velasco

This article proposes a class of goodness-of-fit tests for the autocorrelation function of a time series process, including those exhibiting long-range dependence. Test statistics for composite hypotheses are functionals... Read more...

Keywords: nonparametric model checking ; spectral distribution ; linear processes ; martingale decomposition ; local alternatives ; omnibus ; smooth and directional tests ; long-range alternatives

Semiparametric Estimation for Stationary Processes whose Spectra have an Unknown Pole

We consider the estimation of the location of the pole and memory parameter, ?⁰ and a respectively, of covariance stationary linear processes whose spectral density function f(?) satisfies f(?) ~ C|? - ?<sup>0... Read more...

Keywords: spectral density estimation ; long memory processes ; gaussian processes

Efficiency Improvements in Inference on Stationary and Nonstationary Fractional Time Series

We consider a time series model involving a fractional stochastic component, whose integration order can lie in the stationary/invertible or nonstationary regions and be unknown, and additive deterministic component cons... Read more...

November 2004

Keywords: fractional processes ; efficient semiparametric estimation ; adaptive estimation ; nonstationary processes ; series estimation ; m-estimation

Forecasting the density of asset returns

Trino-Manuel Niguez and Javier Perote

In this paper we introduce a transformation of the Edgeworth-Sargan series expansion of the Gaussian distribution, that we call Positive Edgeworth-Sargan (PES). The main advantage of this new density is that it is well d... Read more...

October 2004

Keywords: density forecasting ; edgeworth-sargan distribution ; probability integral transformations ; p-value plots ; var

Cointegration in Fractional Systems with Deterministic Trends

Fabrizio Iacone and Peter M Robinson

We consider a cointegrated system generated by processes that may be fractionally integrated, and by additive polynomial and generalized polynomial trends. In view of the consequent competition between stochastic and det... Read more...

Keywords: fractional cointegration ; deterministic trends ; ordinary least squares estimation ; generalized least squares estimation ; wald tests.

Nonparametric Inference for Unbalanced Time Series Data

Oliver Linton

This paper is concerned with the practical problem of conducting inference in a vector time series setting when the data is unbalanced or incomplete. In this case, one can work only with the common sample, to which a sta... Read more...

Keywords: bootstrap ; efficient ; hac estimation ; missing data ; subsampling.

ROBUST COVARIANCE MATRIX ESTIMATION: 'HAC' Estimates with Long Memory/Antipersistence Correction

Smoothed nonparametric estimates of the spectral density matrix at zero frequency have been widely used in econometric inference, because they can consistently estimate the covariance matrix of a partial sum of a possibl... Read more...

Keywords: covariance matrix estimation ; long memory ; antipersistence correction ; hac estimates ; vector process ; spectral density.

The Distance between Rival Nonstationary Fractional Processes

Asymptotic inference on nonstationary fractional time series models, including cointegrated ones, is proceeding along two routes, determined by alternative definitions of nonstationary processes. We derive bounds for the... Read more...

Keywords: nonstationary fractional processes ; memory parameter estimation ; fractional cointegration ; rates of convergence.

Consistent Testing for Stochastic Dominance under General Sampling Schemes

Oliver Linton, Esfandiar Maasoumi and Yoon-Jae Whang

We propose a procedure for estimating the critical values of the extended Kolmogorov-Smirnov tests of Stochastic Dominance of arbitrary order in the general K-prospect case. We allow for the observations to be serially d... Read more...

December 2003

Keywords: bootstrap ; dominance ; kolmogorov-smirnov ; subsampling.

A Quantilogram Approach to Evaluating Directional Predictability

Oliver Linton and Yoon-Jae Whang

In this note we propose a simple method of measuring directional predictability and testing for the hypothesis that a given time series has no directional predictability. The test is based on the correlogram of quantile ... Read more...

November 2003

Keywords: correlogram ; dependence ; efficient markets ; quantiles.

A Bootstrap Causality Test for Covariance Stationary Processes

This paper examines a nonparametric test for Granger-causality for a vector covariance stationary linear process under, possibly, the presence of long-range dependence. We show that the test converges to a non-distributi... Read more...

Keywords: causality tests ; long range ; bootstrap tests.

Nonparametric Estimation of Homothetic and Homothetically Separable Functions

Arthur Lewbel and Oliver Linton

For vectors x and w, let r(x,w) be a function that can be nonparametrically estimated consistently and asymptotically normally. We provide consistent, asymptotically normal estimators for the functions g and h, where r(x... Read more...

October 2003

Keywords: cost function ; economic scale ; homogeneous function ; homothetic function ; index models ; nonparametric ; production function ; separability.

LARCH, Leverage and Long Memory

Liudas Giraitis, Remigijus Leipus, Peter M Robinson and Donatas Surgailis

We consider the long memory and leverage properties of a model for the conditional variance of an observable stationary sequence, where the conditional variance is the square of an inhomogeneous linear combination of pas... Read more...

Keywords: leverage ; long memory ; linear arch ; larch ; finiteness of moments.

A Local Instrumental Variable Estimation Method for Generalized Additive Volatility Models

We investigate a new separable nonparametric model for time series, which includes many ARCH models and AR models already discussed in the literature. We also propose a new estimation procedure based on a localization of... Read more...

Keywords: arch ; kernel estimation ; nonparametric ; volatility.

Nonparametric Neural Network Estimation of Lyapunov Exponents and a Direct Test for Chaos

Oliver Linton and Mototsugu Shintani

This paper derives the asymptotic distribution of the nonparametric neural network estimator of the Lyapunov exponent in a noisy system. Positivity of the Lyapunov exponent is an operational definition of chaos. We intro... Read more...

Keywords: artificial neural networks ; nonlinear dynamics ; nonlinear time series ; nonparametric regression ; sieve estimation

Semiparametric Regression Analysis under Imputation for Missing Response Data

Wolfgang Haerdle, Oliver Linton and Qihua Wang

We develop inference tools in a semiparametric regression model with missing response data. A semiparametric regression imputation estimator, a marginal average estimator and a (marginal) propensity score weighted estima... Read more...

Keywords: asymptotic normality ; empirical likelihood ; semiparametric imputation.

Estimating Semiparametric ARCH (8) Models by Kernel Smoothing Methods

We investigate a class of semiparametric ARCH(8) models that includes as a special case the partially nonparametric (PNP) model introduced by Engle and Ng (1993) and which allows for both flexible dynamics and flexible f... Read more...

Keywords: arch ; inverse problem ; kernel estimation ; news impact curve ; nonparametric regression ; profile likelihood ; semiparametric estimation ; volatility

An Alternative Bootstrap to Moving Blocks for Time Series Regression Models

The purpose of this paper is to introduce and examine two alternative, although similar, approaches to the Moving Blocks and subsampling Bootstraps to bootstrapping the estimator of the parameters for time series regress... Read more...

Keywords: least squares estimation ; long-range estimation ; bootstrap methods.

Asymptotic Expansions for Some Semiparametric Program Evaluation Estimators

Hidehiko Ichimura and Oliver Linton

We investigate the performance of a class of semiparametric estimators of the treatment effect via asymptotic expansions. We derive approximations to the first two moments of the estimator that are valid to 'second order... Read more...

Keywords: bandwidth selection ; kernel estimation ; program evaluation ; semiparametric estimation ; treatment effect.

Estimation of Semiparametric Models when the Criterion Function is not Smooth

Xiaohong Chen, Oliver Linton and Ingrid Van Keilegom

We provide easy to verify sufficient conditions for the consistency and asymptotic normality of a class of semiparametric optimization estimators where the criterion function does not obey standard smoothness conditions ... Read more...

Keywords: empirical processes ; non-smooth criterion ; semiparametric estimation ; stochastic equicontinuity.

Cointegration in Fractional Systems with Unkown Integration Orders

The semiparametric local Whittle or Gaussian estimate of the long memory parameter is known to have especially nice limiting distributional properties, being asymptotically normal with a limiting variance that is complet... Read more...

February 2003

Keywords: fractional cointegration ; unknown integration orders ; system estimates ; mixed normal asymptotics.

Edgeworth Expansions for Semiparametric Whittle Estimation of Long Memory

Liudas Giraitis and Peter M Robinson

September 2002

Keywords: edgeworth expansion ; long memory ; semiparametric estimation.

Denis Sargan: Some Perspectives

We attempt to present Denis Sargan's work in some kind of historical perspective, in two ways. First, we discuss some previous members of the Tooke Chair of Economic Science and Statistics, which was founded in 1859 and ... Read more...

Keywords: denis sargan ; tooke chair of economic science and statistics ; asymptotic theory and large models ; semiparametric econometrics.

Higher-Order Kernel Semiparametric M-Estimation of Long Memory

Marc Henry and Peter M Robinson

Econometric interest in the possibility of long memory has developed as a flexible alternative to, or compromise between, the usual short memory or unit root prescriptions, for example in the context of modelling cointeg... Read more...

Keywords: long memory ; semiparametric methods ; higher-order kernel ; m-estimation ; bias ; mean-squared error.

More Efficient Kernel Estimation in Nonparametric Regression with Autocorrelated Errors

Raymond J Carroll, Oliver Linton, Enno Mammen and Zhijie Xiao

We propose a modification of kernel time series regression estimators that improves efficiency when the innovation process is autocorrelated. The procedure is based on a pre-whitening transformation of the dependent vari... Read more...

Keywords: backfitting ; efficiency ; kernel estimation ; time series.

Nonparametric Neutral Network Estimation of Lyapunov Exponents and a Direct Test for Chaos

This paper derives the asymptotic distribution of nonparametric neural network estimator of the Lyapunov exponent in a noisy system proposed by Nychka et al (1992) and others. Positivity of the Lyapunov exponent is an op... Read more...

Keywords: artificial neural networks ; nonlinear dynamics ; nonlinear time series ; nonparametric regression ; sieve estimation.

Consistent Testing for Stochastic Dominance: A Subsampling Approach

We study a very general setting, and propose a procedure for estimating the critical values of the extended Kolmogorov-Smirnov tests of First and Second Order Stochastic Dominance due to McFadden (1989) in the general k-... Read more...

Keywords: prospect theory ; stochastic dominance ; stochastic equicontinuity ; subsampling.

Consistent Order Selection with Strongly Dependent Data and its Application to Efficient Estimation

Order selection based on criteria by Akaike (1974), AIC, Schwarz (1978), BIC or Hannan and Quinn (1979) HIC is often applied in empirical examples. They have been used in the context of order selection of weakly dependen... Read more...

February 2002

Keywords: order selection ; distributed lag models ; strong dependence.

Adapting to Unknown Disturbance Autocorrelation in Regression with Long Memory

Javier Hidalgo and Peter M Robinson

We show that it is possible to adapt to nonparametric disturbance auto-correlation in time series regression in the presence of long memory in both regressors and disturbances by using a smoothed nonparametric spectrum e... Read more...

September 2001

Keywords: time series regression ; long memory ; adaptive estimation.

Gaussian Estimation of Parametric Spectral Density with Unknown Pole

Liudas Giraitis, Javier Hidalgo and Peter M Robinson

We consider a parametric spectral density with power-law behaviour about a fractional pole at the unknown frequency w. The case of unknown w, especially w = 0, is standard in the long memory literature. When w is unknown... Read more...

August 2001

Keywords: long-range dependence ; unknown pole.

Determination of Cointegrating Rank in Fractional Systems

Peter M Robinson and Yoshihiro Yajima

This paper develops methods of investigating the existence and extent of cointegration in fractionally integrated systems. We focus on stationary series, with some discussion of extension to nonstationarity. The setting ... Read more...

Keywords: fractional cointegration ; long memory.

Finite Sample Improvement in Statistical Inference with I(1) Processes

D Marinucci and Peter M Robinson

Robinson and Marinucci (1998) investigated the asymptotic behaviour of a narrow-band semiparametric procedure termed Frequency Domain Least Squares (FDLS) in the broad context of fractional cointegration analysis. Here w... Read more...

Keywords: fully-modified ordinary least squares ; finite sample improvements ; statistical inference with i(1) processes ; monte carlo study ; parametric estimates.

Narrow-Band Analysis of Nonstationary Processes

The behaviour of averaged periodograms and cross-periodograms of a broad class of nonstationary processes is studied. The processes include nonstationary ones that are fractional of any order, as well as asymptotically s... Read more...

Keywords: nonstationary processes ; long-range dependence ; least squares estimation ; narrow-band estimation ; cointegration analysis.

Semiparametric Fractional Cointegration Analysis

Fractional cointegration is viewed from a semiparametric viewpoint as a narrow-band phenomenon at frequency zero. We study a narrow-band frequency domain least squares estimate of the cointegrating vector, and related se... Read more...

Keywords: semiparametric analysis ; fractional cointegration.

A Nonparametric Regression Estimator that Adapts to Error Distribution of Unknown Form

Oliver Linton and Zhijie Xiao

We propose a new estimator for nonparametric regression based on local likelihood estimation using an estimated error score function obtained from the residuals of a preliminary nonparametric regression. We show that our... Read more...

Keywords: adaptive estimation ; asymptotic expansions ; efficiency ; kernel ; local likelihood estimation ; nonparametric regression.

Prediction and Signal Extraction of Strong Dependent Processess in the Frequency Domain

Javier Hidalgo and Yoshihiro Yajima

We frequently observe that one of the aims of time series analysts is to predict future values of the data. For weakly dependent data, when the model is known up to a finite set of parameters, its statistical properties ... Read more...

Keywords: prediction ; strong dependence ; spectral density function ; canonical factorization ; signal extraction.

Parametric Estimation under Long-Range Dependence

Parametric estimation is discussed in a variety of models exhibiting long-range dependence.... Read more...

Keywords: parametric estimation ; long-range dependence.

The Estimation of Conditional Densities

Xiaohong Chen, Oliver Linton and Peter M Robinson

We discuss a number of issues in the smoothed nonparametric estimation of kernel conditional probability density functions for stationary processes. The kernel conditional density estimate is a ratio of joint and margina... Read more...

Keywords: conditional density estimation ; serial dependence ; bandwidth choice.

Estimating Multiplicative and Additive Hazard Functions by Kernel Methods

Oliver Linton, Jens Perch Nielsen and Sara van de Geer

We propose new procedures for estimating the univariate quantities of interest in both additive and multiplicative nonparametric marker dependent hazard models. We work with a full counting process framework that allows ... Read more...

February 2001

Keywords: additive model ; censoring ; kernel ; proportional hazards ; survival analysis

The Memory of Stochastic Volatility Models

A valid asymptotic expansion for the covariance of functions of multivariate normal vectors is applied to approximate autovariances of time series generated by nonlinear transformation of Gaussian latent variates, and no... Read more...

Keywords: stochastic volatility ; long memory ; nonlinear functions of gaussian processes

The Averaged Periodogram for Nonstationary Vector Time Series

Averaged periodogram; nonstationary processes; fractional Brownian motion.... Read more...

December 2000

Keywords: averaged periodogram ; nonstationary processes ; fractional brownian motion.

Whittle Estimation of ARCH Models

For a class of parametric ARCH models, Whittle estimation based on squared observations is shown to be inconsistent and asymptotically normal. Our conditions require the squares to have short memory autocorrelation, by c... Read more...

November 2000

Keywords: arch models ; whittle estimation.

Testing of Seasonal Fractional Integration in UK and Japanese Consumption and Income

L A Gil-Alaña and Peter M Robinson

The seasonal structure of quarterly UK and Japanese consumption and income is examined by means of fractionally-based tests proposed by Robinson (1994). These series were analysed from an autoregressive unit root viewpoi... Read more...

Keywords: fractional integration ; nonstationarity ; seasonality

Limit Theorems for Estimating the Parameters of Differentiated Product Demand Systems

Steve Berry, Oliver Linton and Ariel Pakes

We provide an asymptotic distribution theory for a class of Generalized Method of Moments estimators that arise in the study of differentiated product markets when the number of observations is associated with the number... Read more...

Keywords: choice models ; method of moments ; multinominal ; random coefficients ; vertical model

Edgeworth Approximations for Semiparametric Instrumental Variable Estimators and Test Statistics

We stablish the validity of higher order asymptotic expansions to the distribution of a version of the nonlinear semiparametric instrumental variable considered in Newey (1990) as well as to the distribution of a Wald st... Read more...

Keywords: bandwidth selection ; edgeworth approximation ; instrumental viariables ; kernel estimation ; local polynomials.

Testing the Capital Asset Pricing Model Efficiently under Elliptical Symmetry: A Semiparametric Approach

Douglas J Hodgson, Oliver Linton and Keith Vorkink

Adaptive estimation; capital asset pricing model; efficiency... Read more...

Keywords: adaptive estimation ; capital asset pricing model ; efficiency

Nonparametric Estimation with Aggregated Data

We introduce a kernel-based estimator of the density function and regression function for data that have been grouped into family totals. We allow for a common intra-family component but require that observations from di... Read more...

Keywords: aggregated data ; deconvolution ; grouped data ; kernel ; nonparametric regression

Simulated Asymptotic Least Squares Theory

Ramdan Dridi

We develop in this paper a general econometric methodology referred to as the Simulated Asymptotic Least Squares (SALS). It is shown that this approach provides a unifying theory for 'approximation-based' or simulation-b... Read more...

Keywords: simulated asymptotic least squares ; approximation-based and simulation-based estimation ; efficiency bounds in direction ; gmm ; snls ; spml ; smm ; ii ; gii ; emm.

Noise and Competition in Strategic Oligopoly

Ramdan Dridi and Laurent Germain

Focusing on homogeneous beliefs, we can distinguish two commonly shared ideas that, i) the competition between informed traders destroys their trading profits, ii) trading with a noisy signal brings about a loss in the e... Read more...

Keywords: competition ; optimal noise ; price manipulation

Semi-Parametric Indirect Inference

Ramdan Dridi and Eric Renault

We develop in this paper a generalization of the Indirect Inference (II) to semi-parametric settings and termed Semi-parametric Indirect Inference (SII). We introduce a new notion of Partial Encompassing which lays the e... Read more...

Keywords: indirect inference ; partial encompassing ; pseudo-true value of interest ; structural models ; instrumental models ; wald encompassing tests.

Whittle Pseudo-Maximum Likelihood Estimation for Nonstationary Time Series - (Now published in 'Journal of the American Statistical Association', 95, (2000), pp.1229-1243.)

Whittle pseudo-maximum likelihood estimates of parameters for stationary time series have been found to be consistent and asumptotically normal in the presence of long-range dependence. Generalizing the definition of the... Read more...

Keywords: long-range dependence ; nonstationary long memory time series ; nonstationary fractional models ; frequency domain estimation ; tapering.

Edgeworth Expansions for Spectral Density Estimates and Studentized Sample Mean - (Now published in 'Economic Theory', 17 (2001), pp.497-539.

We establish valid Edgeworth expansions for the distribution of smoothed nonparametric spectral estimates, and of studentized versions of linear statistics such as the same mean, where the studentization employs such a n... Read more...

Keywords: edgeworth expansions ; nonparametric spectral estimates ; stationary gaussian series ; studentized sample mean ; bandwidth choice.

Nonparametric Censored and Truncated Regression

The nonparametric censored regression model, with a fixed, known censoring point (normalized to zero), is y = max[0,m(x) + e], where both the regression function m(x) and the distribution of the error e are unknown. This... Read more...

Keywords: semiparametric ; nonparametric ; censored regression ; truncated regression ; tobit ; latent variable

Adaptive Varying-Coefficient Linear Models

Zongwu Cai, Jianqin Fan and Qiwei Yao

Varying-coefficient linear models arise from multivariate nonparametric regression, nonlinear time series modelling and forecasting, functional data analysis, longitudinal data analysis, and others. It has been a common ... Read more...

Keywords: akaike information criterion ; backfitting algorithm ; generalised cross-validation ; local linear regression ; local significant variable selection ; one-step estimation ; smoothing index ; varying-coefficient linear models.

Nonparametric Test for Causality with Long-Range Dependence - (Now published in 'Econometrica', 68, (2000) pp.1465-1490.

This paper introduces a nonparametric Granger-causality test for covariance stationary linear processes under, possibly, the presence of long-range dependence. We show that the test is consistent and has power against co... Read more...

Keywords: causality ; long-range dependence ; spectral analysis ; distributed lag model ; consistent test

The Existence and Asymptotic Properties of a Backfitting Projection Algorithm under Weak Conditions

Oliver Linton, Enno Mammen and N Nielsen

We derive the asymptotic distribution of a new backfitting procedure for estimating the closest additive approximation to a nonparametric regression function. The procedure employs a recent projection interpretation of p... Read more...

Keywords: additive models ; alternating projections ; backfitting ; kernel smoothing ; local polynomials ; nonparametric regression.

Yield Curve Estimation by Kernel Smoothing Methods

Oliver Linton, Enno Mammen, Jens Perch Nielsen and C Tanggaard

We introduce a new method for the estimation of discount functions, yield curves and forward curves from government issued coupon bonds. Our approach is nonparametric and does not assume a particular functional form for ... Read more...

Keywords: coupon bonds ; kernel estimation ; hilbert space ; nonparametric regression ; term structure estimation ; yield curve ; zero coupon.

Stationarity and Memory of ARCH Models

Paolo Zaffaroni

Sufficient conditions for strict stationarity of ARCH(8) are established, without imposing covariance stationarity and for any specification of the conditional second moment coefficients. GARCH(p,q) as well as the case o... Read more...

Keywords: arch (8) ; garch(p ; q) ; nonlinear moving average representation ; strict and weak stationarity ; memory.

A Model for Long Memory Conditional Heteroscedasticity - (Now published in 'Annals of Applied Probability', 10 (2000), pp.1002-1024.)

Liudas Giraitis, Peter M Robinson and Donatas Surgailis

Keywords: long-range dependence ; semiparametric model ; rates of convergence ; adaptive bandwidth selection.

On Intercept Estimation in the Sample Selection Model

Marcia M Schafgans

We provide a proof of the consistency and asymptotic normality of the estimator suggested by Heckman (1990) for the intercept of a semiparametrically estimated sample selection model. The estimator is based on 'identific... Read more...

January 2000

Keywords: asymptotic normality ; sample selection model ; semiparametric estimation

Adaptive Semiparametric Estimation of the Memory Parameter - (Now published with revised title, Adaptive Rate-Optimal Estimation of the Memory Parameter, in 'Journal of Multivariate Analysis, 72 (2000), pp.183-207.)

Liudas Giraitis, Peter M Robinson and Alexander Samarov

Keywords: long-range dpendence ; semiparametric model ; rates of convergence ; adaptive bandwidth selection.

Contemporaneous Aggregation of GARCH Processes

We study the impact of large cross-sections of contemporaneous aggregation of GARCH processes and of dynamic GARCH factor models. The results crucially depend on the shape of the cross-sectional distribution of the GARCH... Read more...

Keywords: contemporaneous aggregation ; garch ; conditionally heteroskedastic factor models ; common and idiosyncratic risk ; nonlinearity ; memory

Studentization in Edgworth Expansions for Estimates of Semiparametric Index Models - (Now published in C Hsiao, K Morimune and J Powell (eds): 'Nonlinear Statistical Modeling' (Festschrift for Takeshi Amemiya), (Cambridge University Press, 2001), pp.197-240.)

We establish valid theoretical and empirical Edgeworth expansions for density-weighted averaged derivative estimates of semiparametric index models.... Read more...

October 1999

Keywords: edgeworth expansions ; semiparametric estimates ; averaged derivatives

Edgeworth Expansions for Semiparametric Averaged Derivatives - (Now published in 'Econometrica', 68 (2000), pp.931-979.)

A valid Edgeworth expansion is established for the limit distribution of density-weighted semiparametric averaged derivative estimates of single index models. The leading term that corrects the normal limit varies in mag... Read more...

Keywords: edgeworth expansion ; semiparametric estimates ; averaged derivatives

Testing for the Presence of a Random Walk in Series with Structural Breaks - (Now published in 'Journal of Time Series Analysis', 22 (2001), pp.127-150.)

Fabio Busetti and Andrew C Harvey

The paper considers tests for the presence of a random walk component in a stationary or trend stationary time series and extends them to series which contain structural breaks. The locally best invariant (LBI) test is d... Read more...

December 1998

Keywords: brownian bridge ; cram?r-von mises distribution ; intervention analysis ; locally best invariant test ; structural time series model ; unobserved components.

Variance-Type Estimation of Long Memory - (Now published in 'Stochastic Processes and their Applications', 29 (1999), pp.1-24.)

The aggregation procedure when a sample of length N is divided into blocks of length m = o(N), m ? ? and observations in each block are replaced by their sample mean, is widely used in statistical inference. Taqqu, Tever... Read more...

October 1998

Keywords: long memory ; aggregation ; semiparametric model

Seasonal and Cyclical Long Memory - (Now published in S Ghosh (ed): 'Asymptotics, Nonparametrics and Time Series: A Tribute to Madam Lal Puri (Marcel Decker, 1999), pp.115-145.)

Josu Artech and Peter M Robinson

There has recently been great interest in time series with long memory, namely series whose dependence decays slowly in the sense that autocovariances are not summable and the spectral density is unbounded. This concept ... Read more...

September 1998

Keywords: long memory ; seasonal time series ; cyclic time series

Semiparametric Inference in Seasonal and Cyclical Long Memory Processes - (Now published in 'Journal of Time Series Analysis', 21 (2000), pp.1-25.)

Several semiparametric estimates of the memory parameter in standard long memory time series are now available. They consider only local behaviour of the spectrum near zero frequency, about which the spectrum is symmetri... Read more...

Keywords: semiparametric inference ; long memory ; seasonality

Long and Short Memory Conditional Heteroscedasticity in Estimating the Memory Parameter of Levels - (Now published in 'Econometric Theory', 15 (1999), pp.299-336.)

Semiparametric estimates of long memory seem useful in the analysis of long financial time series because they are consistent under much broader conditions than parametric estimates. However, recent large sample theory f... Read more...

August 1998

Keywords: long memory ; dynamic conditional heteroscedasticity ; semiparametric estimation

Alternative Forms of Fractional Brownian Motion - (Now published in 'Journal of Statistical Planning and Inference', 80 (1999), pp.111-122.)

It is pointed out that two contradictory definitions of fractional Brownian motion are well established, one prevailing in the probabilistic literature, the other in the econometric literature. Each is associated with a ... Read more...

Keywords: frational brownian motion ; nonstationary time series ; long-range dependence

Band Spectrum Regression for Cointegrated Time Series with Long Memory Innovations

D Marinucci

Band spectrum regression is considered for cointegrated time series with long memory innovations. The estimates we advocate are shown to be consistent when cointegrating relationships among stationary variables are inves... Read more...

Keywords: long-range dependence ; band spectrum regression ; cointegration

Weak Convergence of Multivariate Fractional Processes - (Now published in 'Stochastic Processes and their Applications', 80 (1999), pp.103-120.)

Weak convergence to a form of fractional Brownian motion is established for a wide class of nonstationary fractionally integrated multivariate processes. Instrumental for the main argument is a result of some independent... Read more...

Keywords: nonstationary fractional integration ; functional central limit theorem

Aggregation of Simple Linear Dynamics: Exact Asymptotic Results

Marco Lippi and Paolo Zaffaroni

his paper deal with aggregation of AR(1) micro variables driven by a common and idiosyncratic shock with random coefficients. We provide a rigorous analysis, based on results on sums of r.v.'s with a possibly finite firs... Read more...

Keywords: aggregation ; idiosymcratic-driven fluctuations ; long memory ; nonstationarity.

Semiparametric Frequency Domain Analysis of Fractional Cointegration - (Revised version forthcoming in P M Robinson: 'Time Series with Long Memory' (Oxford University Press).

The concept of cointegration has principally been developed under the assumption that the raw data vector zt is I(1) and the cointegrating residual et is I(0), but is also of interest in more general, including fractiona... Read more...

Keywords: fractional cointegration ; narrow-band frequency analysis

Interpolating Exogenous Variables in Open Continuous Time Dynamic Models

J R McCrorie

As the exact discrete model induced by an open continuous time system depends on the continous time paths of the exogenous variables, these need to be interpolated for the purpose of estimation. We examine some recently ... Read more...

December 1997

Keywords: continous time ; exact discrete model ; aliasing ; exogenous variables ; interpolation

Deriving the Exact Discrete Analog of a Continuous Time System

We present a method of deriving the exact discrete model satisfied by equispaced data generated by a system of linear stochastic differential equations without implying the usual restrictions on observed discrete data th... Read more...

Keywords: continuous time ; exact discrete model ; random measure ; matrix exponential

A Nonparametric Test for I(0) - (Now published in 'Review of Economic Studies', 65 (1998), pp.475-495.)

Ignacio Lobato and Peter M Robinson

There is frequently interest in testing that a scalar or vector time series is I(0), possibly after first- differencing or other detrending, while the I(0) assumption is also taken for granted in autocorrelation-consiste... Read more...

November 1997

Keywords: nonparametric testing ; weak dependence ; long memory

Some Practical Issues in Maximum Simulated Likelihood

In this paper, I explore ways of recapturing the efficiency property for estimators that rely on simulation. In particular, I show that this can be achieved by exploiting two-step maximum stimulated likelihood (SL) estim... Read more...

Keywords: simulation estimation ; maximum simulated likelihood ; limited dependent variable models ; antithetic acceleration.

Inference-without-Smoothing in the Presence of Nonparametric Autocorrelation - (Now published in 'Econometrica', 66 (1998), pp.1163-1182.)

In a number of econometric models, rules of large-sample inference require a consistent estimate of f(0), where f (?) is the spectral density matrix of yt = ut?xt, for covariance stationary vectors ut, xt. Typically yt i... Read more...

October 1997

Keywords: autocorrelation-consistent variance estimation ; long-range dependence ; simultaneous equations systems.

Large-Sample Inference for Nonparametric Regression with Dependent Errors - (Now published in 'Annals of Statistics', 28 (1997), pp.2054-2083.)

A central limit theorem is given for certain weighted sums of a covariance stationary process, assuming it is linear in martingale differences, but without any restriction on its spectrum. We apply the result to kernel n... Read more...

September 1997

Keywords: central limit theorem ; nonparametric regression ; autocorrelation ; long-range dependence

Beta Convergence

C Michelacci and Paolo Zaffaroni

Unit root in output, an exceptional 2% rate of convergence, and no change in the underlying dynamics of output seems to be three stylized facts that can not go together. This paper extends the Solow-Swan growth model all... Read more...

Keywords: growth model ; convergence ; long memory ; aggregation

Gaussian Estimation of Long-Range Dependent Volatility in Asset Prices

Asset returns have a very complicated dynamic pattern. Yet they display regularity across different assets and periods. We consider a new family of volatility models which account for such patterns, focussing in particul... Read more...

Keywords: stochastic volatility ; long memory ; asymptotics.

The Method of Simulated Scores for the Estimation of LDV Models

V A Hajivassiliou and DL McFadden

The method of simulated scores (MSS) is presented for estimating limited dependent variables models (LDV) with flexible correlation structure in the unobservables. We propose simulators that are continuous in the unknown... Read more...

Keywords: limited dependent variable models ; simulation estimation ; gibbs resampling

Messy Time Series: A Unified Approach - (Now published in 'Advances in Econometrics', 13 (1998)pp.103-143.)

Andrew C Harvey, Siem Jan Koopman and J Penzer

Many series are subject to data irregularities such as missing values, outliers, structural breaks and irregular spacing. Data can also be messy, and hence difficult to handle by standard procedures, when they are intrin... Read more...

Keywords: arima models ; data aggregation ; importance sampling ; irregularly spaced data ; kalman filter ; missing observations ; outliers ; smoother ; splines ; state space form ; structural breaks ; structural time series models ; weekly observations.

Semiparametric Estimation of a Sample Selection Model: A Simulation Study

Standard approaches to the estimation of sample selection models are known to be inconsistent under non-normality. In particular, this paper considers the two-step Heckman (1976, 1979) estimator of the interecept of the ... Read more...

Keywords: sample selection models ; semiparametric estimation ; error distributions ; bandwidth parameter ; two-step parametric estimator.

Gender Wage Differences in Malaysia: Parametric and Semiparametric Estimation

This paper is an empirical study on the labor force in (Peninsular) Malaysia. It applies both parametric and semiparametric sample selection methods to the estimation of wage equations. These equations are then used to a... Read more...

Keywords: malaysia ; gender wage differences ; gender discrimination ; wage determining characteristics ; parametric and semiparametric estimation.

Testing Game-Theoretic Models of Price Fixing Behaviour

This paper analyses price fixing by the Joint Executive Committee railroad cartel from 1880 to 1886 and develops tests of two game-theoretic models of tacit collusion. The first model, due to Abreu, Pearce and Stacchetti... Read more...

Keywords: price-fixing ; trigger-price mechanism ; switching regression models ; measurement errors ; simulation estimation.

Rate Optimal Semiparametric Estimation of the Memory Parameter of the Gaussian Time Serieswith Long-Range Dependence - (Now published in 'Journal of Time Series Analysis', 18 (1997), pp.49-60.)

There exist several estimators of the memory parameter in long-memory time series models with mean µ and the spectrum specified only locally near zero frequency. In this paper we give a lower bound for the rate of conver... Read more...

February 1997

Keywords: long-range dependence ; semiparametric models ; optimal rates of convergence ; lower bounds

Nonlinear Time Series with Long Memory: A Model for Stochastic Volatility - (Now published in 'Journal of Statistical Planning and Inference', 68 (1998), pp.359-371.)

We introduce a nonlinear model of stochastic volatility within the class of ?product type? models. It allows different degrees of dependence for the ?raw? series and for the ?squared? series, for instance implying weak d... Read more...

January 1997

Keywords: long memory ; two-shock model ; stochastic volatility

Modelling Nonlinearity and Long Memory in Time Series - (Now published in 'Nonlinear Dynamics and Time Series', C D Cutler and D T Kaplan (eds), Fields Institute Communications, 11 (1997), pp.61-170.)

We discuss models that impart a form of long memory in raw time series xt or instantaneous functions thereof, in particular . on the basis of a linear or nonlinear model. The capacity of linear models for xt to imply lon... Read more...

Keywords: long memory ; arch ; nonlinear moving average. jel no.: c22

Time Series Regression with Long Range Dependence - (Now published in 'Annals of Statistics', 25, (1997)pp.2054-2083.)

A general limit theorem is established for time series regression estimates which include generalized least squares, in the presence of long range dependence in both errors and stochastic regressors. The setting and resu... Read more...

Keywords: long-range dependence ; linear regression ; generalized least squares ; nonlinear regression

Testing of Unit Root and Other Nonstationary Hypotheses in Macroeconomic Time Series - (Now published in 'Journal of Econometrics', 80, 1997, pp.241-268.)

Recently proposed tests for unit root and other nonstationarity of Robinson (1994a) are applied to an extended version of the data set used by Nelson and Plosser (1982). Unusually, the tests are efficient (against approp... Read more...

December 1996

Keywords: nonstationarity ; macroeconomic time series ; fractional integration

Autocorrelation-Robust Inference - (Now published in 'Handbook of Statistics', vol.15, G S Maddala and C R Rao (eds), Elsevier Science Publishers BV (1997), pp.267-298.)

We consider statistical inference in the presence of serial dependence. The main focus is on use of statistics that are constructed as if no dependence were believed present, and are asymptotically normal in the presence... Read more...

Keywords: time series ; variance estimation ; spectral methods

Multivariate Structural Time Series Models - (Now published in 'System Dynamics in Economic and Financial Models', CHeij, H Schumacher, B Hanzon and C Praagman (eds.) John Wiley & Sons, Chichester (1997), pp.269-298.)

Andrew C Harvey and Siem Jan Koopman

Much of economic analysis presupposes that certain economic time series can be decomposed into trends and cycles. Structural time series models are explicitly set up in terms of such unobserved components. This paper set... Read more...

Keywords: co-integration ; commontrends ; cycles ; kalman filter ; structural time series model ; triangular representation

Testing for a Slowly Changing Level with Special Reference to Stochastic Volatility - (Now published in 'Journal of Econometrics', 87 (1998), pp.167-189.)

Andrew C Harvey and Mariane Streibel

A test for the presence of a stationary first-order autoregressive process embedded in white noise is constructed so as to be relatively powerful when the autoregressive parameter is close to one. This is done by setting... Read more...

Keywords: exchange rates ; garch model ; locally best invariant test ; serial correlation ; stochastic volatility ; unobserved components ; von mises distribution.

Nonparametric Estimation with Strongly Dependent Multivariate Time-Series - (Now published in 'Journal of Time Series Analysis',18 (1997)pp.95-122.)

Smooth nonparametric kernel density and regression estimators are studied when the data is strongly dependent. In particular, we derive Central (and Noncentral) Limit Theorems for the kernel density estimator of a multiv... Read more...

February 1996

Keywords: nonparametric ; strong dependence ; hermite and appell polynomials ; rosenblatt and hermite pocesses.

Spectral Analysis for Bivariate Time Series with Long Memory - (Now published in 'Econometric Theory',12 (1997)pp.773-792.)

This paper provides limit theorems for special density matrix estimators and functionals of it for a bivariate co variance stationary process whose spectral density matrix has singularities not only at the origin but pos... Read more...

Keywords: long memory ; spectral density matrix ; spectral estimation ; weighted autocovariance

Aggregate and Regional Disagggregate Fluctuations (Now published in Empirical Economics (1996), vol.21, no.1, pp.137-159.)

This paper models fluctuations in regional disaggregates as a nonstationary, dynamically evolving distribution. Doing so enables study of the dynamics of aggregate fluctuations jointly with those of the rich cross-sectio... Read more...

August 1995

Keywords: aggregate disturbance ; business cycle ; distribution dynamics ; regional fluctuation ; stochastic kernel.

The Modelling and Seasonal Adjustment of Weekly Observations - (Now published in 'Journal of Business and Economic Statistics', 15 (1997), pp.354-368.)

Andrew C Harvey, Siem Jan Koopman and Marco Riani

A number of important economic time series are recorded on a particular day every week. Seasonal adjustment of such series is difficult because the number of weeks varies between 52 and 53 and the position of the recordi... Read more...

Keywords: structural time series model ; seasonal adjustment ; trend extraction ; filtering and smoothin algorithms ; money supply.

Measuring Core Inflation (Now published in Economic Journal, vol. 105, No. 432 (September 1995), pp.1130-1144.)

Danny Quah and Shaun P. Vahey

In this paper, we argue that measured (RPI) inflation is conceptually mismatched with core inflation: the difference is more than just 'measurement error'. We propose a technique for measuring core inflation, based on an... Read more...

Keywords: core inflation ; vector autoregression ; dynamic restrictions

Empirics for Economic Growth and Convergence (Now published in European Economic Review, vol.40, no.6 (1996), pp.1353-1375.)

The convergence hypothesis has generated a huge empirical literature: this paper critically reviews some of the earlier key findings, clarifies their implications, and relates them to more recent results. Particular atte... Read more...

Keywords: evolving distributions ; galton's fallacy ; polarization ; regional dynamics ; stochastic kernel ; unit root.

Convergence Empirics Across Economies with (Some)Capital Mobility (Now published in Journal of Economic Growth, vol.1, No.1 ( March 1996),pp.95-124.)

This paper reinterprets a simple model of growth and fluctuations across many economies to allow explicitly characterizing the dynamically evolving corss-economy distribution of income. Such a framework provides a more n... Read more...

Keywords: growth and fluctuations ; cross-country distribution of income ; convergence hypothesis ; intra-distribution mobility ; capital investment

Exploiting Cross Section Variation for Unit Root Inference in Dynamic Data (Now published in Economics Letters 44 (1), 1994, pp.9-19.)

This paper considers unit root regressions in data having simultaneously extensive cross-section and time-series variation. The standard least-squares estimators in such data structures turn out to have an asymptotic dis... Read more...

Keywords: random field ; time series ; panel data ; unit root

Estimation and Testing of Stochastic Variance Models

Andrew C Harvey and N.G. Shephard

A stochastic variance model may be estimated by quasi-maximum likelihood procedure by transforming to a linear state space form. The properties of observations corrected for heteroscedasticity can be derived. A model wit... Read more...

Keywords: generalised least squares ; heteroscedasticity ; quasi-maximum likelihood ; smoothing ; volatiliy.

Seasonality in Dynamic Regression Models (Now published in Economic Journal 104 (1994), pp.1324-1345.)

Andrew C Harvey and Andrew Scott

This paper examines the implications of treating seasonality as an unobserved component which changes slowly over time. This approach simplifies the specification of dynamic relationships by separating non-seasonal from ... Read more...

Keywords: seasonality ; dynamic relationships ; stable error correction model ; autoregressive models

Galton's Fallacy and Tests of the Convergence Hypothesis (Now published in Scandinavian Journal of Economics 95 (4), 1993, pp.427-443.)

Recent tests for the convergence hypothesis derive from regressing average growth rates on initial levels: a negative initial coefficient is interpreted as convergence. These tests turn out to be plagued by Galton's clas... Read more...

Keywords: convergence hypothesis ; regressing average growth rates ; galton's fallacy ; coefficients of arbitrary signs ; divergence of cross-country incomes.

The Multivariate Invariance Principle for Globally Nonstationary Processes, with an Application to I(2) Models

James Davidson

A multivariate invariance principle is given for dependent processes exhibiting trending variances and other types of global nonstationarity. The limit processes obtained in these results are not Brownian motion, but mem... Read more...

Keywords: multivariate invariance principle ; dependent processes ; trending variances ; global nonstationarity ; gaussian diffusion process ; stochastic integrals ; integrated variables.

Conditions for Strong and Uniform Mixing in Linear Processes

A sufficiency condition for strong mixing in infinite order moving average processes due to Gorodetski (1977) is extended, showing how smoothness conditions on the marginal distributions can be traded off against summabi... Read more...

Keywords: strong mixing ; uniform mixing ; moving avrage process

Deletion Diagnostics and Transformations for Time Series

A.C. Atkinson and N.G. Shephard

Deletion diagnostics are developed for structural time series models. These show the effect of the deletion of individual observations on residuals and on the estimates of regression parameters. The methods are extended ... Read more...

Keywords: deletion diagnostics ; structural time series models ; regression parameters ; index plots.

Quasi-Maximum Likelihood Estimation of Stochastic Variance Models

Esther Ruiz

Changes in variance or volatility over time can be modelled using stochastic volatility (SV) models. This approach is based on treating the variance as an unobservable variable, the logarithm of which is modelled as a li... Read more...

Keywords: stochastic variance models ; volatility ; asymptotic and finite sample properties ; qml estimator ; generalized method of moments ; autoregression.

The Central Limit Theorem for Globally Nonstationary Near-Epoch Dependent Functions of Mixing Processes: The Asymptotically Degenerate Case (Now published in Economic Theory 9 (1993), pp.402-412.)

The central limit theorem in Davidson (1992a) is extended to allow cases where the variances of sequence coordinates can be tending to zero. A trade off is demonstrated between the degree of dependence (mixing size) and ... Read more...

Keywords: central limit theorem ; sequence coordinates ; rate of degeneration ; mixing processes ; martingale difference.

An L1-Convergence Theorem for Heterogeneous Mixingale Arrays with Trending Moments (Now published in Statistics & Probability Letters 16 (1993), pp.301-304.)

This paper gives a generalization of an L1-convergence theorem for dependent processes due to Andrews (1988). Among the cases covered by this result are weak laws of large numbers of random sequences {X1} having moments ... Read more...

Keywords: l1-convergence theorem ; heterogeneous mixingale arrays ; weak laws of large numbers ; random sequences ; trending moments.

Exact Score for Time Series Models in State Space Form (Now published in Biometrika (1992), 79, 4, pp.283-6.)

Siem Jan Koopman and N.G. Shephard

The score vector for a time series model which fits into the Gaussian state space form can be approximated by numerically differentiating the log-likelihood. If the parameter vector is of length p, this involves the runn... Read more...

Keywords: smoothing ; kahman filter ; em algorithm ; unobserved components model ; profile likelihood.

Detrending, Stylized Facts and The Business Cycle (Now published in Journal of Applied Econometrics, vol.8 (1993), pp.231-247.)

Andrew C Harvey and Albert Jaeger

The stylized facts of macroeconomic time series can be presented by fitting structural time series models. Within this framework, we analyze the consequences of the widely used detrending technique popularized by Hodrick... Read more...

Keywords: detrending ; filters ; persistence ; structural time series models.

A Local Scale Model: An Unobserved Component Alternative to Integrated GARCH Processes (Now published in Journal of Econometrics, vol.60, (1994), pp.181-202.)

N.G. Shephard

Central Limit Theorems for Nonstationary Mixing Processes and Near-Epoch Dependent Functions (Now published in Economic Theory, vol.8, no.3 (1992).)

Cointegration in recursive systems: the structure of wage and price determination in the united kingdom (now published in economic journal res/aute 1990 conference supplement, vol.101, march 1991, pp.239-251.).

James Davidson and Stephen Hall

Least-Squares Autoregression with Near-Unit Root

Jan R. Magnus and Thomas J. Rothenberg

The Exact Multiperiod Mean-Square Forecast Error for the First-Order Autoregressive Model with an Intercept (Now published in Journal of Econometrics, 42, (1989), pp.157-179.)

Jan R. Magnus and Bahram Pesaran

The Bias of Forecasts from a First-Order Autoregression (Now published in Econometric Theory, 7, (1991), pp.222-235.)

Cointegration in linear dynamic systems (now published in journal of time series analysis, 12,1 (1991), pp.41-62.), the exact multiperiod mean-square forecast error for the first-order autoregressive model (now published in journal of econometrics, 39, (1988), pp.327-346.).

Asraul Hoque, Jan R. Magnus and Bahram Pesaran

The Exact Moments of a Ratio of Quadratic Forms in Normal Variables (Now published in Annales d'Economie et de Statistique, Vol.1 (1986).)

Jan R. Magnus

A Note on Instrumental Variables and Maximum Likelihood Estimation Procedures (Now published in Annales d'Economie et de Statistique, 10, (1988), pp.121-138.)

Alberto Holly and Jan R. Magnus

Some Properties of the Bordered Hessian Matrix (Now published in Advanced Lectures in Quantitative Economics (ed. F. van der Ploeg), (Academic Press, London, 1990), pp.583-604.)

Some evidence on the robustness of nonlinear fiqml, symmetry, 0-1 matrices, and jacobians: a review (now published in econometric theory, vol.2 (1986).).

Jan R. Magnus and H. Neudecker

Interfuel substitution and separability in Dutch Manufacturing: A Multivariate Error Components Approach (Now published in Applied Economics, 19, (1987), pp.1639-1664.)

Jan R. Magnus and Alan D. Woodland

Money Disequilibrium: An Approach to Modelling Monetary Phenomena in the U.K. (Now published in The Operation and Regulation of Financial Markets, edited by Charles Goodhart, David Llewellyn and David Currie, (Macmillan, 1987).)

A generalization of the univariate logit model and its bivariate extension.

Takamitsu Sawa

On Differentiating Eigenvalues and Eigenvectors (Now published in Econometric Theory, Vol.1 (1985).)

Asymptotic normality of the maximum likelihood estimation in the nonlinear regression model with normal errors (now published in econometric theory, vol.2 (1986) pp.374-412.).

Risto D.H. Heijmans and Jan R. Magnus

Error Correction Systems

Consistent maximum likelihood estimation of the nonlinear regression model with normal errors (now published in journal of econometrics, vol.32 (1986).), on the asymptotic normality of the maximum likelihood estimator with dependent observations (now published in statistica neerlandia, vol.40 (1986).), econometric modelling of the sterling effective exchange rate (now published in review of economic studies, lii (1985), pp.231 240.), on the consistency of the maximum likelihood estimator with dependent observations (now published in the journal of econometrics, vol.32 (1986).), money demand stability in the u.k. and error correction mechanism.

Manfred Keil

Computer Price Functions (Now published in Oxford Bulletin of Economics and Statistics, Vol.45, No.4, (1983), pp.339-356.)

Anthony Horsley and G.M.P. Swann

Alternative Estimates for Systems with Log-Linear Stochastic Equations and Linear Identities

An econometric model of the money supply and balance of payments in the united kingdom.

James Davidson and Manfred Keil

Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of Inflationary Expectations (Now published in Econometrica, Vol.50, July 1982, pp.987-1007.)

Robert F. Engle

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70 Econometrics Research Topics for You

Econometrics is an interesting area of research. However, picking econometric research topics may be a bit difficult. This is because it is a broad aspect of economics with increasing relevance by the day.

Economic relationships can be accurately measured and defined. This involves a certain statistical and mathematical approach known as econometrics. It is the foundation for economic forecasting.

In this article, you will find 70 interesting econometrics topics crafted for you! We have done the hard work, saving you the stress of picking the right topics for your presentation, research work, or seminar. Are you ready? Let’s get started!

Easy Econometrics Paper Topics

Need to write an econometrics research paper? Here are some econometrics research paper topics for you!

• The suggestion of theory- the initial step in econometrics methodology
• How Inflation affects National Savings
• Estimating variables- why it must be done
• The essence of Proof-reading after estimating variables
• Why the need for testing a hypothesis
• How Trade Relates to Economic growth: An in-depth look.
• Analyzing how Income Affects Life Insurance

Econometrics Research Topics for Undergraduates

Need to write an undergraduate econometrics research paper? Here are some econometrics topics suitable for undergraduate research purposes.

• How poverty affects education
• Relationship between childhood obesity and poverty
• Income inequality and human development
• The link between ideologies and religion on the economy of a country
• Importation and income- what is the relationship?
• Life expectancy and personal income- the link
• How minimum wage affects unemployment

Econometrics Topics for a Project

If you are studying something around econometrics at college, you’ll need to do a project! Here are some econometrics research paper topics for your project!

• Can econometrics be used to predict the future?
• Econometrics is a source of numerical estimates for the variables of economic relationships
• How to test economic theories presented by econometricians using econometrics
• What is regression discontinuity, and how can it be applied?
• The role of instrumental variables in econometrics
• How randomized experiments aid econometrics
• The essence of matching methods in econometrics

Interesting Econometrics Research Topics

If econometric research is one of the things that gives you goosebumps, we have added to your excitement. We just saved you the stress of having to look for interesting econometrics research topics. Here are 7 interesting econometrics topics for you!

• A study of customer behavior for green products
• Trade patterns- a research on the various kinds and their applications today
• A study on rare events and their macroeconomics
• A study of the effect of making a business international
• Comparative institutional economics- relevance and impact
• Research on the economies of scale
• A close look at bank regulations and monetary policies

Sports Econometrics Topics

The economics of sport is analyzed using sports econometrics. This makes it easier to make forecasts and accurate predictions. We have selected interesting econometrics topics in the sports niche for you. See them here:

• How a sports team affects the local economy
• The impact of discrimination in sports
• Sports attendance and its economic relevance
• Factors that affect competitive balance in sports and its effect on the economy
• The relationship between threshold efficiency and market competition in sports
• The economics of professional football contracts
• Professional hockey- skill, performance, and earnings

Financial Econometrics Research Topics

There are numerous topics for econometrics research papers. We have made it hassle-free for you to pick one for your next research work. Here are some econometrics topics research papers below.

• Latest statistical tools for financial econometrics
• A study on multiple regression model and its applications
• What to learn from recent financial crises
• Monetary policy- structuring and implementation
• Cashless policy and its impact on the economy
• The loan markets-A critical look and survey
• Bank regulation and policy-the impact on the economy

Micro Econometrics Topics Suggestions

Micro econometrics is an interesting area to make a presentation. To this end, we have picked out seven relevant topics in econometrics that pertain to micro econometrics. See them below!

• The methods and applications of micro econometrics
• Micro econometric modeling
• The role of the labor market in econometrics
• How labor supply impacts the local economy
• What is the spillover effect, and how does it affect econometrics?
• The application of panel data methods- a subsection of microeconomics
• Micro behavioral theory

Advanced Topics in Econometrics

Need some advanced econometrics topics? Here are some advanced econometrics research topics just for you.

• The study and application of theoretical econometrics
• General line model- definition, application, and relevance
• Applied econometrics- the key to converting qualitative economic statements into quantitative ones
• How productivity and goods production affects econometrics at the national level
• The theory of arbitrage pricing
• The impact of high or low demand of labor on the economy
• Theoretical statistics vs. Analytical statistics

Good Simple Econometric Research Topics

Need some good econometrics topics that are easy to work on? Below are some simple topics in econometrics for your research!

• The various concepts of stochastic processes
• Accurate prediction of stochastic processes
• Line time series model- its definition, estimation, and application
• The purpose of dynamic econometric models
• Multiple time series model- a critical approach to determine its application
• The relevance of vector autoregressive processes
• Causality and impulse-response analysis

Receive Your Econometrics Research Paper

Want someone to hand over simple research paper topics to you? The following are econometric paper topics selected for you!

• The state of the national economy
• InterInternal trades and local economic status
• The impact of importation and exportation on the local economy
• Supply and demand forecast
• A critique on international banks
• The foreign exchange market-how it relates to local businesses
• Business monopolies in the nation- a critical study

If you’re pursuing a thesis in the field of econometrics and find yourself in need of expert support, you might want to consider seeking professional assistance for writing your thesis. Our reputable academic writing services provide specialized help that can alleviate the challenges you may encounter during the thesis writing process. By requesting us to ‘ write my thesis for me ,’ you can tap into the expertise of experienced writers who possess in-depth knowledge of econometrics. Our experts will guide you through the intricacies of data analysis, statistical modeling, and hypothesis testing, ensuring that your thesis adheres to the highest academic standards

We believe you now have more than enough topics in econometrics in your arsenal. With these interesting econometric topics, you can make the best seminar presentations or research around econometrics! Have fun!

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102 Best Econometrics Research Topics

College and university professors require students to write about econometrics research topics to gauge their comprehension of the relationship between mathematical economics, statistics, and economics.

The purpose of this integration is to provide numerical values to economic relationships and parameters. Usually, econometrics involves economic theories and their presentation in mathematical forms and the empirical study of business. Perhaps, this integration explains why some students struggle to choose topics for research in econometrics.

What Is Econometrics?

As hinted, econometrics is an economics branch that focuses on the relationships between economics, statistics, and mathematical economics. Ideally, econometrics entails the quantitative application of mathematical and statistical models using data to test hypotheses and develop economic theories while forecasting future trends based on historical data. Econometricians subject real-world data to various statistical trials while comparing and contrasting the results against the idea under examination.

Writing an econometric research paper is a process that starts with the selection of an interesting topic. Once you’ve chosen a title and the supervisor approves it, embark on extensive research using the prompt from your teacher. Proceed by gathering and analyzing all relevant information from different sources. Engaging in in-depth study and comprehensive analysis will enable you to write an informative paper that will compel the educator to award you the best grade in your class. Below are the steps to follow to write a high-quality econometric thesis or essay.

Write the introduction: Introduce your econometrics topic and tell the audience why it’s crucial. Also, include a thesis statement summarizing the entire paper. Describe the theoretical model: Tell the readers about the theoretical models to structure the empirical work. Present the data: Describe the data, whether time series or cross-sectional. Use descriptive statistics data and graphics if possible. Present the empirical model: Explain the model you intend to estimate and the functional form you intend to use. Present your empirical results: This section presents empirical results using a table to summarize them. Conclude the paper: Describe lessons from the research and state whether it supports the theory. Also, suggest approaches for future research on the topic.

Your paper should also include a reference section comprising the information sources you used to gather data.

Interesting Econometrics Paper Topics

Maybe you know the process of writing a paper on an econometrics topic but don’t have an idea to explore. If so, consider these exciting econometrics paper ideas.

• How privatizing public enterprises could affect economic development and policy
• Cashless economy: How demonetization affects medium and small businesses
• How Gini index dynamics reflect the income inequality problem
• Consumption evolution over the last decade: Consumer behavior and trends
• Investigating salary inequalities and the forces behind them
• How income changes affect consumer choices
• How does allowing the labor force to participate in public budgeting affect the economy
• How the marital status affect the labor force composition
• How consumption attitudes have changed over the last decade
• How economic convergence relates to salary levels
• How income affects life insurance
• The consequences of leaving the rat race
• Testing Okun’s Law in the U.S
• Analysis of spending on disposable income and imports
• Comparing the unemployment rate in the United States to the rest of the world
• Regional labor mobility and unemployment
• Stock market evolution: Analyzing the causes and effects
• How internet productivity relate to connectivity in the workplace
• How currency devaluation affects medium and small companies
• How government spending and inflation relate in an economy
• The relationship between stock prices and inflation in a country
• How income tax revenue affects a developing economy
• How government expenditure affects economic growth
• Factors contributing to the global recession
• How a country’s unemployment rate relates to economic growth

Any of these topics can be an excellent basis for an econometrics paper. However, you require extensive research about any of these topics to develop a winning thesis.

Undergraduate Econometrics Project Ideas

Maybe your school or faculty requires you to write an econometrics paper to graduate from university. In that case, consider these econometrics research topics for undergraduates.

• Analyzing the impact of income inequality on the poverty level
• Analyzing gender differences in education between developing and developed countries
• How immigration affects unemployment in the European Union
• How economic growth relates to trade
• Are immigrants more in countries with a high income?
• How high taxations affect GSP
• Analyzing the relationship between local income level and house prices
• How income, education, and life expectancy affect the human development index
• How inflation affects national savings
• How life expectancy relates to national income
• How financial development affects the economic growth of a country
• Crime index versus the average education years
• Investigating the correlation between youth unemployment and minimum wage
• How economic prosperity relate to government systems
• Economic factors that affect housing prices in the United States
• Economic factors contributing to homelessness in the U.S
• Socioeconomic and economic determinants of infant mortality
• Econometric analysis: Impact of trade barriers
• Why matching methods are essential in econometrics
• How a randomized experiment can aid econometrics
• Why instrumental variables matter in econometrics
• Can experts predict the future using econometrics?
• Econometrics as a numerical estimates source for economic relationship variables
• Ways of testing economic theories that econometricians present
• Regression discontinuity: Describe its application

These are great ideas to consider for an econometrics project. Nevertheless, you require sufficient time to research any of these topics and write a winning essay or dissertation.

Easy Econometric Research Topics

Perhaps, you need an easy topic for an econometrics paper. Maybe you have a short time to complete your assignment. In that case, these econometrics topics are ideal for you.

• Theory suggestion- The initial econometrics methodology step
• Why estimating variables is important
• The importance of Proof-reading once you have evaluated the variables
• Why testing a hypothesis matters
• The impact of poverty on education
• How poverty relates to childhood obesity
• Human development and income inequality
• The link between religion and ideologies on a country’s economy
• Income and importation- How do they connect?
• Personal income and life expectancy- What is the connection?
• The effects of minimum wage on unemployment
• Investigating monetary policies and bank regulations
• A study of the economies of scale
• The impact and relevance of comparative institutional economics
• Analyzing the effect of making a company international
• Studying the macroeconomics of rare events
• Investigating customer behavior towards green products
• Trade patterns: Investigating different trade patterns and their applications
• Different stochastic processes concepts
• Accurate stochastic processes prediction

Any of these topics can be a sound basis of a simple paper. Nevertheless, you still require time to research the idea and analyze data to develop a quality paper.

Financial Econometrics Research Paper Topics

Perhaps, you want to write an academic paper about a financial econometrics topic. If so, consider these ideas.

• How does bank regulation affect the economy?
• A critical look into the loan markets
• How a cashless policy affects the economy
• Structure and implementation of the monetary policy
• Lessons to learn from financial crises
• Investigating regression models
• Statistical tools in the financial econometrics

These are good topics to explore in financial econometrics. However, follow the prompt from your teacher to write an impressive paper.

Econometrics Empirical Project Ideas FExor Ph.D. Level

Maybe you’re pursuing your Ph.D. and want to write a dissertation about an econometrics topic. In that case, this category comprises excellent ideas for you.

• Analytical statistics versus theoretical statistics
• The effects of the low and high demand of labor on an economy
• The arbitrage pricing theory
• How goods production and productivity affect econometrics at a national level
• Applied econometrics- Its essence in turning qualitative economic ideas into quantitative ones
• Definition, relevance, and application of the general line model
• Theoretical econometrics’ study and application
• The macro behavioral theory
• Panel data methods applications- A microeconomics subsection
• The impacts of the spillover effect on econometrics
• The impact of labor supply on a local economy
• Why labor markets are essential to econometrics
• What is micro-econometrics modeling?
• Micro-econometrics methods and applications
• Statistical tools and their use in financial econometrics

This list also has fantastic economics paper topic ideas. But like the topics in the other sections, each of these notions requires extensive research to write a quality paper.

Exciting Econometrics Questions

Maybe you need a question to serve as the basis of your econometrics research. In that case, here are exciting queries to inspire you.

• What is the current state of your country’s economy?
• What’s the difference between the current state of the local and international trades?
• What are the latest forecasts for the global economy?
• How do the foreign exchange market and the local businesses relate?
• What’s the impact of exportation and importation on the local economy?
• How do businesses monopolies affect a country’s economy?
• What are the effects of international banks on the local banking sector?
• How does population growth affect economic development?
• How can a natural disaster affect an emerging economy?
• What techniques do companies use to “nudge” consumers into spending more?

This comprehensive list has some of the best econometrics ideas for essays and research papers. Nevertheless, having a topic is not a guarantee that you’ll write a good essay. You might still need help with your assignment after choosing a topic.

Get Help With Thesis About Econometrics Topic

Our crew comprises the most skilled, talented, and experienced econometrics writers. These professionals have helped many students complete their econometrics papers on varied topics. If stuck with an econometric essay or an MBA thesis , for example, and require a cheap dissertation writing service , our native, educated experts can help you. We’re the most knowledgeable econometrics writers online. Contact us now to get a custom, high-quality research paper on any econometrics topic!

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Home » Term Paper – Format, Examples and Writing Guide

Term Paper – Format, Examples and Writing Guide

Table of Contents

Definition:

Term paper is a type of academic writing assignment that is typically assigned to students at the end of a semester or term. It is usually a research-based paper that is meant to demonstrate the student’s understanding of a particular topic, as well as their ability to analyze and synthesize information from various sources.

Term papers are usually longer than other types of academic writing assignments and can range anywhere from 5 to 20 pages or more, depending on the level of study and the specific requirements of the assignment. They often require extensive research and the use of a variety of sources, including books, articles, and other academic publications.

Term Paper Format

The format of a term paper may vary depending on the specific requirements of your professor or institution. However, a typical term paper usually consists of the following sections:

• Title page: This should include the title of your paper, your name, the course name and number, your instructor’s name, and the date.
• Abstract : This is a brief summary of your paper, usually no more than 250 words. It should provide an overview of your topic, the research question or hypothesis, your methodology, and your main findings or conclusions.
• Introduction : This section should introduce your topic and provide background information on the subject. You should also state your research question or hypothesis and explain the importance of your research.
• Literature review : This section should review the existing literature on your topic. You should summarize the key findings and arguments made by other scholars and identify any gaps in the literature that your research aims to address.
• Methodology: This section should describe the methods you used to collect and analyze your data. You should explain your research design, sampling strategy, data collection methods, and data analysis techniques.
• Results : This section should present your findings. You can use tables, graphs, and charts to illustrate your data.
• Discussion : This section should interpret your findings and explain what they mean in relation to your research question or hypothesis. You should also discuss any limitations of your study and suggest areas for future research.
• Conclusion : This section should summarize your main findings and conclusions. You should also restate the importance of your research and its implications for the field.
• References : This section should list all the sources you cited in your paper using a specific citation style (e.g., APA, MLA, Chicago).
• Appendices : This section should include any additional materials that are relevant to your study but not essential to your main argument (e.g., survey questions, interview transcripts).

Structure of Term Paper

Here’s an example structure for a term paper:

I. Introduction

A. Background information on the topic

B. Thesis statement

II. Literature Review

A. Overview of current literature on the topic

B. Discussion of key themes and findings from literature

C. Identification of gaps in current literature

III. Methodology

A. Description of research design

B. Discussion of data collection methods

C. Explanation of data analysis techniques

IV. Results

A. Presentation of findings

B. Analysis and interpretation of results

C. Comparison of results with previous studies

V. Discussion

A. Summary of key findings

B. Explanation of how results address the research questions

C. Implications of results for the field

VI. Conclusion

A. Recap of key points

B. Significance of findings

C. Future directions for research

VII. References

A. List of sources cited in the paper

How to Write Term Paper

Here are some steps to help you write a term paper:

• Choose a topic: Choose a topic that interests you and is relevant to your course. If your professor has assigned a topic, make sure you understand it and clarify any doubts before you start.
• Research : Conduct research on your topic by gathering information from various sources such as books, academic journals, and online resources. Take notes and organize your information systematically.
• Create an outline : Create an outline of your term paper by arranging your ideas and information in a logical sequence. Your outline should include an introduction, body paragraphs, and a conclusion.
• Write a thesis statement: Write a clear and concise thesis statement that states the main idea of your paper. Your thesis statement should be included in your introduction.
• Write the introduction: The introduction should grab the reader’s attention, provide background information on your topic, and introduce your thesis statement.
• Write the body : The body of your paper should provide supporting evidence for your thesis statement. Use your research to provide details and examples to support your argument. Make sure to organize your ideas logically and use transition words to connect paragraphs.
• Write the conclusion : The conclusion should summarize your main points and restate your thesis statement. Avoid introducing new information in the conclusion.
• Edit and proofread: Edit and proofread your term paper carefully to ensure that it is free of errors and flows smoothly. Check for grammar, spelling, and punctuation errors.
• Format and cite your sources: Follow the formatting guidelines provided by your professor and cite your sources properly using the appropriate citation style.
• Submit your paper : Submit your paper on time and according to the instructions provided by your professor.

Term Paper Example

Here’s an example of a term paper:

Title : The Role of Artificial Intelligence in Cybersecurity

As the world becomes more digitally interconnected, cybersecurity threats are increasing in frequency and sophistication. Traditional security measures are no longer enough to protect against these threats. This paper explores the role of artificial intelligence (AI) in cybersecurity, including how AI can be used to detect and respond to threats in real-time, the challenges of implementing AI in cybersecurity, and the potential ethical implications of AI-powered security systems. The paper concludes with recommendations for organizations looking to integrate AI into their cybersecurity strategies.

Introduction :

The increasing number of cybersecurity threats in recent years has led to a growing interest in the potential of artificial intelligence (AI) to improve cybersecurity. AI has the ability to analyze vast amounts of data and identify patterns and anomalies that may indicate a security breach. Additionally, AI can automate responses to threats, allowing for faster and more effective mitigation of security incidents. However, there are also challenges associated with implementing AI in cybersecurity, such as the need for large amounts of high-quality data, the potential for AI systems to make mistakes, and the ethical considerations surrounding the use of AI in security.

Literature Review:

This section of the paper reviews existing research on the use of AI in cybersecurity. It begins by discussing the types of AI techniques used in cybersecurity, including machine learning, natural language processing, and neural networks. The literature review then explores the advantages of using AI in cybersecurity, such as its ability to detect previously unknown threats and its potential to reduce the workload of security analysts. However, the review also highlights some of the challenges associated with implementing AI in cybersecurity, such as the need for high-quality training data and the potential for AI systems to be fooled by sophisticated attacks.

Methodology :

To better understand the challenges and opportunities associated with using AI in cybersecurity, this paper conducted a survey of cybersecurity professionals working in a variety of industries. The survey included questions about the types of AI techniques used in their organizations, the challenges they faced when implementing AI in cybersecurity, and their perceptions of the ethical implications of using AI in security.

The results of the survey showed that while many organizations are interested in using AI in cybersecurity, they face several challenges when implementing these systems. These challenges include the need for high-quality training data, the potential for AI systems to be fooled by sophisticated attacks, and the difficulty of integrating AI with existing security systems. Additionally, many respondents expressed concerns about the ethical implications of using AI in security, such as the potential for AI to be biased or to make decisions that are harmful to individuals or society as a whole.

Discussion :

Based on the results of the survey and the existing literature, this paper discusses the potential benefits and risks of using AI in cybersecurity. It also provides recommendations for organizations looking to integrate AI into their security strategies, such as the need to prioritize data quality and to ensure that AI systems are transparent and accountable.

Conclusion :

While there are challenges associated with implementing AI in cybersecurity, the potential benefits of using these systems are significant. AI can help organizations detect and respond to threats more quickly and effectively, reducing the risk of security breaches. However, it is important for organizations to be aware of the potential ethical implications of using AI in security and to take steps to ensure that these systems are transparent and accountable.

References:

• Alkhaldi, S., Al-Daraiseh, A., & Lutfiyya, H. (2019). A Survey on Artificial Intelligence Techniques in Cyber Security. Journal of Information Security, 10(03), 191-207.
• Gartner. (2019). Gartner Top 10 Strategic Technology Trends for 2020. Retrieved from https://www.gartner.com/smarterwithgartner/gartner-top-10-strategic-technology-trends-for-2020/
• Kshetri, N. (2018). Blockchain’s roles in meeting key supply chain management objectives. International Journal of Information Management, 39, 80-89.
• Lipton, Z. C. (2018). The mythos of model interpretability. arXiv preprint arXiv:1606.03490.
• Schneier, B. (2019). Click Here to Kill Everybody: Security and Survival in a Hyper-Connected World. WW Norton & Company.
• Wahab, M. A., Rahman, M. S., & Islam, M. R. (2020). A Survey on AI Techniques in Cybersecurity. International Journal of Scientific & Engineering Research, 11(2), 22-27.

When to Write Term Paper

A term paper is usually a lengthy research paper that is assigned to students at the end of a term or semester. There are several situations when writing a term paper may be required, including:

• As a course requirement: In most cases, a term paper is required as part of the coursework for a particular course. It may be assigned by the instructor as a way of assessing the student’s understanding of the course material.
• To explore a specific topic : A term paper can be an excellent opportunity for students to explore a specific topic of interest in-depth. It allows them to conduct extensive research on the topic and develop their understanding of it.
• To develop critical thinking skills : Writing a term paper requires students to engage in critical thinking and analysis. It helps them to develop their ability to evaluate and interpret information, as well as to present their ideas in a clear and coherent manner.
• To prepare for future academic or professional pursuits: Writing a term paper can be an excellent way for students to prepare for future academic or professional pursuits. It can help them to develop the research and writing skills necessary for success in higher education or in a professional career.

Purpose of Term Paper

The main purposes of a term paper are:

• Demonstrate mastery of a subject: A term paper provides an opportunity for students to showcase their knowledge and understanding of a particular subject. It requires students to research and analyze the topic, and then present their findings in a clear and organized manner.
• Develop critical thinking skills: Writing a term paper requires students to think critically about their subject matter, analyzing various sources and viewpoints, and evaluating evidence to support their arguments.
• Improve writing skills : Writing a term paper helps students improve their writing skills, including organization, clarity, and coherence. It also requires them to follow specific formatting and citation guidelines, which can be valuable skills for future academic and professional endeavors.
• Contribute to academic discourse : A well-written term paper can contribute to academic discourse by presenting new insights, ideas, and arguments that add to the existing body of knowledge on a particular topic.
• Prepare for future research : Writing a term paper can help prepare students for future research, by teaching them how to conduct a literature review, evaluate sources, and formulate research questions and hypotheses. It can also help them develop research skills that they can apply in future academic or professional endeavors.

Advantages of Term Paper

There are several advantages of writing a term paper, including:

• In-depth exploration: Writing a term paper allows you to delve deeper into a specific topic, allowing you to gain a more comprehensive understanding of the subject matter.
• Improved writing skills: Writing a term paper involves extensive research, critical thinking, and the organization of ideas into a cohesive written document. As a result, writing a term paper can improve your writing skills significantly.
• Demonstration of knowledge: A well-written term paper demonstrates your knowledge and understanding of the subject matter, which can be beneficial for academic or professional purposes.
• Development of research skills : Writing a term paper requires conducting thorough research, analyzing data, and synthesizing information from various sources. This process can help you develop essential research skills that can be applied in many other areas.
• Enhancement of critical thinking : Writing a term paper encourages you to think critically, evaluate information, and develop well-supported arguments. These skills can be useful in many areas of life, including personal and professional decision-making.
• Preparation for further academic work : Writing a term paper is excellent preparation for more extensive academic projects, such as a thesis or dissertation.

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Economics Term Papers Samples For Students

566 samples of this type

WowEssays.com paper writer service proudly presents to you an open-access catalog of Economics Term Papers meant to help struggling students tackle their writing challenges. In a practical sense, each Economics Term Paper sample presented here may be a pilot that walks you through the important phases of the writing process and showcases how to compose an academic work that hits the mark. Besides, if you need more visionary assistance, these examples could give you a nudge toward a fresh Economics Term Paper topic or inspire a novice approach to a banal theme.

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Economics Of China Term Paper Examples

Good what is the current macroeconomic situation in the u.s term paper example, good example of honduras term paper, part 1: background information, egypt term papers examples, part.1 country background:.

- Neighbouring Countries: Gaza, Israel, Libya, Sudan, - Territorial Size of the Country: 1,002, 450 square kilometres - Size of the Population: 86,000,000 (2014 estimate) - Type of Political System: Democratic System, multi-party system - Who is the Prime Minister, President?

Acting Prime Minister: Ibrahim Mahlab

Good term paper on data warehouse.

Thesis statement: Data warehousing is critical in the socio-economic success of businesses in the world today by means of providing an important platform for both social and economic interactions that proceed with the greatest regard to competitiveness and relevance since markets continue to grow more unfavorable to irrelevant businesses. It plays a central role in ensuring the existence and competitiveness of an organization remains intact as efficiency and reliability of data processing are upheld. Organizations have to adopt data warehousing if at all they need to remain relevant in the current super competitive markets.

Good History Term Paper Example

Gender relations term paper examples.

Gender relations have only slightly changed in the midst of the growing complexity of human societies through the centuries. Women remain the primary caregivers and are expected by their society to be responsible for child and infant care whether it be in subsistence economies or in modern day stateshood. However, men’s traditional roles of hunting, construction, political leadership, and even combat have slowly become shared with women as societies become politically and economically more complex.

Subsistence economies

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World Economic Situation and Prospects 2024

Global economic growth is projected to slow from an estimated 2.7 per cent in 2023 to 2.4 per cent in 2024, trending below the pre-pandemic growth rate of 3.0 per cent, according to the United Nations World Economic Situation and Prospects (WESP) 2024. This latest forecast comes on the heels of global economic performance exceeding expectations in 2023. However, last year’s stronger-than-expected GDP growth masked short-term risks and structural vulnerabilities. 

The UN’s flagship economic report presents a sombre economic outlook for the near term. Persistently high interest rates, further escalation of conflicts, sluggish international trade, and increasing climate disasters, pose significant challenges to global growth.

The prospects of a prolonged period of tighter credit conditions and higher borrowing costs present strong headwinds for a world economy saddled with debt, while in need of more investments to resuscitate growth, fight climate change and accelerate progress towards the Sustainable Development Goals (SDGs).

“2024 must be the year when we break out of this quagmire. By unlocking big, bold investments we can drive sustainable development and climate action, and put the global economy on a stronger growth path for all,” said António Guterres, United Nations Secretary-General. “We must build on the progress made in the past year towards an SDG Stimulus of at least $500 billion per year in affordable long-term financing for investments in sustainable development and climate action.”

Subdued growth in developed and developing economies Growth in several large, developed economies, especially the United States, is projected to decelerate in 2024 given high interest rates, slowing consumer spending and weaker labour markets. The short-term growth prospects for many developing countries – particularly in East Asia, Western Asia and Latin America and the Caribbean – are also deteriorating because of tighter financial conditions, shrinking fiscal space and sluggish external demand. Low-income and vulnerable economies are facing increasing balance-of-payments pressures and debt sustainability risks. Economic prospects for small island developing States, in particular, will be constrained by heavy debt burdens, high interest rates and increasing climate-related vulnerabilities, which threaten to undermine, and in some cases, even reverse gains made on the SDGs.

Inflation trending down but recovery in labour markets still uneven Global inflation is projected to decline further, from an estimated 5.7 per cent in 2023 to 3.9 per cent in 2024. Price pressures are, however, still elevated in many countries and any further escalation of geopolitical conflicts risks renewed increases in inflation. 

In about a quarter of all developing countries, annual inflation is projected to exceed 10 per cent in 2024, the report highlights. Since January 2021, consumer prices in developing economies have increased by a cumulative 21.1 per cent, significantly eroding the economic gains made following the COVID-19 recovery. Amid supply-side disruptions, conflicts and extreme weather events, local food price inflation remained high in many developing economies, disproportionately affecting the poorest households. 

“Persistently high inflation has further set back progress in poverty eradication, with especially severe impacts in the least developed countries,” said Li Junhua, United Nations Under-Secretary-General for Economic and Social Affairs. “It is absolutely imperative that we strengthen global cooperation and the multilateral trading system, reform development finance, address debt challenges and scale up climate financing to help vulnerable countries accelerate towards a path of sustainable and inclusive growth.”

According to the report, the global labour markets have seen an uneven recovery from the pandemic crisis. In developed economies, labour markets have remained resilient despite a slowdown in growth. However, in many developing countries, particularly in Western Asia and Africa, key employment indicators, including unemployment rates, are yet to return to pre-pandemic levels. The global gender employment gap remains high, and gender pay gaps not only persist but have even widened in some occupations.   

Related Sustainable Development Goals

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The Driving Forces Behind the Great Migration: an Analysis of Economic, Social, and Political Factors

This essay about the Great Migration discusses the significant movement of African Americans from the rural South to urban areas in the North, Midwest, and West during the early 20th century through the 1970s. It explores the economic, social, and political impacts of this migration, highlighting the search for better opportunities, the fight against racial oppression, and the transformation of American cities. Despite facing challenges like discrimination and urban poverty, this migration fostered cultural renaissances and significant demographic and political changes.

How it works

The saga of the Great Migration is a compelling chapter in American history, highlighting a significant period when millions of African Americans left the agrarian South for the urban centers of the North, Midwest, and West from the early 20th century to the 1970s. This mass movement fundamentally transformed the United States’ economic, social, and political frameworks, making a lasting impact on the nation’s collective identity.

Economically, the Great Migration was driven by the search for better opportunities beyond the South, where the legacy of slavery and racial discrimination limited prospects for African Americans.

Trapped in the cycles of sharecropping and tenant farming, and oppressed under Jim Crow laws that enforced segregation and disenfranchisement, the North offered an alternative with its industrial boom. Cities like Chicago and Detroit promised industrial jobs with better wages, drawing those seeking to break free from poverty and oppression.

Socially, the migration represented a journey towards freedom from racial oppression and systemic injustice. In the South, African Americans were terrorized by lynching and the Ku Klux Klan, living in a constant state of fear and persecution. They were marginalized through segregation, denied quality education, housing, and basic civil rights. Although the North was not free from racism, it offered more opportunities for social mobility and better access to resources, drawing many in pursuit of respect and equality.

Politically and culturally, the Great Migration occurred against a backdrop of significant upheavals. The early Civil Rights Movement, with leaders like W.E.B. Du Bois and Booker T. Washington, pushed for racial equality and social justice. Both World Wars disrupted traditional labor markets, providing African Americans with new job opportunities and accelerating demographic changes.

However, the migration also came with significant challenges. Migrants faced continued discrimination in the North, with prevalent housing segregation and job discrimination. Urban ghettos emerged, marked by poverty and social strife, challenging the initial optimism of the migrants and straining family and community ties.

Despite these obstacles, the Great Migration spurred significant cultural, economic, and political shifts. It nurtured the growth of African American communities in northern cities, leading to cultural renaissances like Harlem’s. It contributed to the development of an urban black middle class and significantly altered the political landscape of major cities, impacting elections and setting the stage for later civil rights achievements.

Overall, the Great Migration stands as a testament to the resilience and determination of African Americans in their ongoing struggle for a better life, embodying the fight against racial inequality and highlighting the need for reflection and reconciliation in America’s pursuit of equality and unity.

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The Driving Forces Behind the Great Migration: An Analysis of Economic, Social, and Political Factors. (2024, May 21). Retrieved from https://papersowl.com/examples/the-driving-forces-behind-the-great-migration-an-analysis-of-economic-social-and-political-factors/

"The Driving Forces Behind the Great Migration: An Analysis of Economic, Social, and Political Factors." PapersOwl.com , 21 May 2024, https://papersowl.com/examples/the-driving-forces-behind-the-great-migration-an-analysis-of-economic-social-and-political-factors/

PapersOwl.com. (2024). The Driving Forces Behind the Great Migration: An Analysis of Economic, Social, and Political Factors . [Online]. Available at: https://papersowl.com/examples/the-driving-forces-behind-the-great-migration-an-analysis-of-economic-social-and-political-factors/ [Accessed: 22 May. 2024]

"The Driving Forces Behind the Great Migration: An Analysis of Economic, Social, and Political Factors." PapersOwl.com, May 21, 2024. Accessed May 22, 2024. https://papersowl.com/examples/the-driving-forces-behind-the-great-migration-an-analysis-of-economic-social-and-political-factors/

"The Driving Forces Behind the Great Migration: An Analysis of Economic, Social, and Political Factors," PapersOwl.com , 21-May-2024. [Online]. Available: https://papersowl.com/examples/the-driving-forces-behind-the-great-migration-an-analysis-of-economic-social-and-political-factors/. [Accessed: 22-May-2024]

PapersOwl.com. (2024). The Driving Forces Behind the Great Migration: An Analysis of Economic, Social, and Political Factors . [Online]. Available at: https://papersowl.com/examples/the-driving-forces-behind-the-great-migration-an-analysis-of-economic-social-and-political-factors/ [Accessed: 22-May-2024]

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Call for Papers - African Economic Conference 2024

May 20, 2024.

The 2024 African Economic Conference (AEC), jointly organized by the African Development Bank (AfDB), the Economic Commission for Africa (ECA), and the United Nations Development Programme (UNDP), will be held in Gaborone, Botswana, from 23-25 November 2024. 

Since its inception in 2006, the AEC series has fostered research, expert analysis, policy dialogue, and the exchange of knowledge on various issues and challenges facing Africa.

The theme of this year’s conference is “Securing Africa’s Economic Future Amidst Rising Uncertainty,” with a focus on the following four subthemes:

Global, regional, and national uncertainty impacts on Africa's development prospects: Global, regional, and national uncertainties can disrupt trade, investment flows, and economic growth. Geopolitical tensions, such as ongoing conflicts and shifting of alliances, add another layer of complexity to the continent's economic landscape. These tensions can lead to trade restrictions, sanctions, and alterations in global supply chains, affecting African economies dependent on a limited range of exports. Additionally, global financial market volatility, influenced by fluctuating interest rates and international relations, can contribute to economic instability. African economies, particularly those reliant on commodities like oil, minerals, and agricultural products, are susceptible to commodity price shocks. This volatility can trigger inflation, destabilize national currencies, and strain economies with substantial external debt, thus, reducing their capacity to finance development. The COVID-19 pandemic has compounded these challenges, disrupting global trade and investment flows. It has also reduced tourism revenues and strained healthcare systems, exacerbating economic difficulties. 

Such questions aim to foster a deep dive into the complexities and nuances of Africa's economic landscape amid global uncertainties, promoting a proactive discussion on resilience and economic policies under uncertainties. 

Policy-oriented papers linked to the conference's overall theme are of particular interest.

Submission Guidelines:

Interested authors should submit their papers to  rba.aec @undp.org  by 15 August 2024.

Only full papers addressing the conference theme will be considered for presentation. We encourage the submission of policy- and solution-oriented papers with strong empirical foundation.

Authors are asked to submit their papers and register according to the following schedule:

Submission of papers:      15 August 2024 

Notification of acceptance:     30 September 2024

Last day for registration for presenters:     20 October 2024

Conference dates:     23-25 November 2024

Young African researchers are especially encouraged to submit their articles. One of the objectives of the AEC series is to provide young African researchers the opportunity to not only share their work with a broader audience but also expand their networks.

Format Guidelines for Papers

Review process:.

All papers will be blind peer-reviewed by the co-organizing institutions. Only original, quality work will be accepted for presentation.

A distinguished panel will assess the papers presented and award a prize to a young author whose work is deemed the best conference paper. The winner will be announced at the closing ceremony.

Sponsorship:

Authors should indicate whether they require support to cover expenses (travel, accommodation, and daily subsistence allowance) associated with their participation at the conference. Only one author per paper accepted for presentation will be eligible for sponsorship. Support is reserved mainly for presenters and young researchers from Africa.

For further information, please read the 2024 African Economic Conference concept note . 

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