Foundations and Trends® in Econometrics > Vol 3 > Issue 1

Nonparametric Econometrics: A Primer

Jeffrey S. Racine, Department of Economics, McMaster University, racinej@mcmaster.ca
 
Suggested Citation
Jeffrey S. Racine (2008), "Nonparametric Econometrics: A Primer", Foundations and TrendsĀ® in Econometrics: Vol. 3: No. 1, pp 1-88. http://dx.doi.org/10.1561/0800000009

Published: 01 Mar 2008
© 2008 J. S. Racine
 
Subjects
Semiparametric and nonparametric estimation
 

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In this article:
1. Introduction
2. Density and Probability Function Estimation
3. Conditional Density Estimation
4. Regression
5. Semiparametric Regression
6. Panel Data Models
7. Consistent Hypothesis Testing
8. Computational Considerations
9. Software
Conclusions
Acknowledgments
Background Material
Notations and Acronyms
References

Abstract

This review is a primer for those who wish to familiarize themselves with nonparametric econometrics. Though the underlying theory for many of these methods can be daunting for some practitioners, this article will demonstrate how a range of nonparametric methods can in fact be deployed in a fairly straightforward manner. Rather than aiming for encyclopedic coverage of the field, we shall restrict attention to a set of touchstone topics while making liberal use of examples for illustrative purposes. We will emphasize settings in which the user may wish to model a dataset comprised of continuous, discrete, or categorical data (nominal or ordinal), or any combination thereof. We shall also consider recent developments in which some of the variables involved may in fact be irrelevant, which alters the behavior of the estimators and optimal bandwidths in a manner that deviates substantially from conventional approaches.

DOI:10.1561/0800000009
ISBN: 978-1-60198-110-3
100 pp. $75.00
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ISBN: 978-1-60198-111-0
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Table of contents:
1. Introduction
2. Density and Probability Function Estimates
3. Conditional Density Estimation
4. Regression
5. Semiparametric Regression
6. Panel Data Models
7. Consistent Hypothesis Testing
8. Computational Considerations
9. Software
Conclusions
Acknowledgments
Background Material
Notations and Acronyms
References

Nonparametric Econometrics

Nonparametric Econometrics is a primer for those who wish to familiarize themselves with nonparametric econometrics. While the underlying theory for many of these methods can be daunting for practitioners, this monograph presents a range of nonparametric methods that can be deployed in a fairly straightforward manner.

Nonparametric methods are statistical techniques that do not require a researcher to specify functional forms for objects being estimated. The methods surveyed are known as kernel methods, which are becoming increasingly popular for applied data analysis. The appeal of nonparametric methods stems from the fact that they relax the parametric assumptions imposed on the data generating process and let the data determine an appropriate model.

Nonparametric Econometrics focuses on a set of touchstone topics while making liberal use of examples for illustrative purposes. The author provides settings in which the user may wish to model a dataset comprised of continuous, discrete, or categorical data (nominal or ordinal), or any combination thereof. Recent developments are considered, including some where the variables involved may in fact be irrelevant, which alters the behavior of the estimators and optimal bandwidths in a manner that deviates substantially from conventional approaches.

 
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