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Applied Nonparametric Econometrics Paperback – March 12, 2015
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"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 University
"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
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, discussing basic to advanced nonparametric methods with applications.
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1.) Density Estimation,
2.) Regression Analysis,
3.) Discrete data handling, and
4.) Other advanced non-parametric, semiparametric methods, Instrumental variables etc.
This book gives state-of-the-art techniques and can be used as a reference in implementing and trying out examples by our own. If you are familiar (or willing to learn) R (language), you will love this book.
I'm coming from an Electrical Engineering background (which proves the fact this book can be used even for students outside the Field of Economics) and the theory in the book helped me to think out-of-the-box, specially on non-parametric regression techniques (somewhat different techniques we study under typical machine learning lectures).
All in all, it is a good book to learn nonparametric techniques.
Very good treatment of nonparametrics. Examples and applications are very helpful. Although, I would have liked to see some about series/sieve methods.