Springer Semiparametric and Nonparametric Methods in Econometrics (Springer Series in Statistics)

This text emphasizes the main ideas underlying a variety of nonparametric and semiparametric methods. This edition contains over one hundred pages of new material as well as empirical examples to illustrate the methods presented. Review From the reviews: “This book presents the main ideas underlying a variety of non parametric and semiparametric estimation methods in a most intuitive way. … appropriate for students in economic and social sciences with a solid background knowledge of statistics and/or econometrics. … It is certainly also accessible to applied researchers … . I can definitely also recommend it for lectures in master courses … . it is probably the most felicitous I have read.” (Stefan Sperlich, Journal of the American Statistical Association, Vol. 106 (493), March, 2011) “This book is intended to introduce graduate students and researchers to nonparametric and semiparametric methods and their applications to econometrics. … all results are stated with the appropriate conditions and the role of the conditions is explained. The book is a nice survey of useful results for an applied researcher.” (Lajos Horváth, Mathematical Reviews, Issue 2010 j) From the Back Cover Standard methods for estimating empirical models in economics and many other fields rely on strong assumptions about functional forms and the distributions of unobserved random variables. Often, it is assumed that functions of interest are linear or that unobserved random variables are normally distributed. Such assumptions simplify estimation and statistical inference but are rarely justified by economic theory or other a priori considerations. Inference based on convenient but incorrect assumptions about functional forms and distributions can be highly misleading. Nonparametric and semiparametric statistical methods provide a way to reduce the strength of the assumptions required for estimation and inference, thereby reducing the opportunities for obtaining misleading results. These methods are applicable to a wide variety of estimation problems in empirical economics and other fields, and they are being used in applied research with increasing frequency. The literature on nonparametric and semiparametric estimation is large and highly technical. This book presents the main ideas underlying a variety of nonparametric and semiparametric methods. It is accessible to graduate students and applied researchers who are familiar with econometric and statistical theory at the level taught in graduate-level courses in leading universities. The book emphasizes ideas instead of technical details and provides as intuitive an exposition as possible. Empirical examples illustrate the methods that are presented. This book updates and greatly expands the author’s previous book on semiparametric methods in econometrics. Nearly half of the material is new. Joel L. Horowitz is the Charles E. and Emma H. Morrison Professor of Market Economics at Northwestern University. He is the author of over 100 journal articles and book chapters in econometrics and statistics, a winner of the Richard Stone prize in applied econometrics, a fellow of the Econometric Society and American Statistical Association, and a former co-editor of Econometrica.