Nonparametric Econometrics: Theory and Practice

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Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers.

Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data--nominal and ordinal--in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory.

This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types--continuous, nominal, and ordinal--within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables.

Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.

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Nonparametric Econometrics: Theory and Practice Review

I have been through the first four chapters of the book now with a student in some detail. This is a very nice book. It is clearly written and covers the frontier of the literature. It is not appropriate as a first treatment of non-parametrics as it presumes some background knowledge. A reader new to the area should start with something a bit more applied such as the older Silverman or Haerdle books. This book is also more proof-oriented than the Pagan and Ullah book. I think of this book as oriented toward an advanced graduate course or as a reference book for those who use these methods and want to understand their theoretical econometric foundations. The book is particulary strong, not surprisingly, on things that the authors have written a lot about, such as kernel regression and cross-validation.

Disclaimer: I know both Jeff and Qi.

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