The majority of empirical research in economics ignores the potential benefits of nonparametric methods, while the majority of advances in nonparametric theory ignores the problems faced in applied econometrics. This book helps bridge this gap between applied economists and theoretical nonparametric econometricians. It discusses in depth, and in terms that someone with only one year of graduate econometrics can understand, basic to advanced nonparametric methods. The analysis starts with density estimation and motivates the procedures through methods that should be familiar to the reader. It then moves on to kernel regression, estimation with discrete data, and advanced methods such as estimation with panel data and instrumental variables models. The book pays close attention to the issues that arise with programming, computing speed, and application. In each chapter, the methods discussed are applied to actual data, paying attention to presentation of results and potential pitfalls.Read more
- Explains nonparametric econometrics at a level most economists can understand
- Discusses how to properly apply nonparametric methods
- Presents new material in what is a rapidly advancing field
Reviews & endorsements
"A clear and thorough treatment of nonparametric and semiparametric econometrics. The text will be valuable to empirical researchers, who can expand their methodological toolkits without resorting to difficult journal articles. Even advanced topics, such as nonparametric instrumental variables and nonparametric models with panel data, are treated at an accessible level."
Jeffrey M. Wooldridge, Michigan State UniversitySee more reviews
"Taking theory to data is difficult for most students, but this book provides substantial help by providing cogent explanations of practical considerations, including how well methods that work "in theory" might be expected to work with real data in the quantities that researchers might have available."
Paul W. Wilson, Clemson University
"Daniel Henderson and Chris Parmeter have provided a modern survey of nonparametric econometrics. Newcomers will enjoy their applications-oriented introduction to this growing field. Theorists will find a compact survey of the most important foundations. Researchers of all sorts will want to add this valuable resource to their libraries."
William Greene, Stern School of Business, New York University
"This well-written textbook represents a rigorous yet accessible introduction to nonparametric methods, one that makes clear the importance of these techniques for empirical research. Henderson and Parmeter have performed a valuable service for students throughout the social sciences."
Steven N. Durlauf, University of Wisconsin
"Nonparametric econometric methods have by now become quite common in applied research, yet, as in almost all areas of research, theory precedes practice. The current hands-on approach of the book comes to fill the gap and offer the applied researcher a manual of how to properly use these methods without compromising rigor. It will complement other more theoretical books on the subject and as such it will prove very useful to many practitioners and students alike."
Thanasis Stengos, University of Guelph
"The authors advertise right at the beginning that this book was written to help bridge the gap between applied economists and theoretical nonparametric econometricians. Having worked on both sides I can say that this book keeps this promise in almost all aspects: the way it is written, the selection of topics, and the selection of methods."
Stefan Sperlich, Université de Genève
"The aim of this book is to teach nonparametric methods to applied economists. The book does an excellent job of achieving this objective. The mix of rigor and intuition is perfect, and the availability of software to go with the book makes it easy to implement the techniques being taught."
Peter Schmidt, Michigan State University
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- Date Published: January 2015
- format: Paperback
- isbn: 9780521279680
- length: 380 pages
- dimensions: 251 x 178 x 28 mm
- weight: 0.73kg
- contains: 81 b/w illus. 34 tables
- availability: Available
Table of Contents
2. Univariate density estimation
3. Multivariate density estimation
4. Inference about the density
6. Testing in regression
7. Smoothing discrete variables
8. Regression with discrete covariates
9. Semiparametric methods
10. Instrumental variables
11. Panel data
12. Constrained estimation and inference
An Interview with Daniel J. Henderson and Christopher F. Parmeter
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