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Nonparametric Econometrics (Themes in Modern Econometrics) Paperback – June 13, 1999
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"This book contains nonparametric concepts for information recovery that will become an enduring element of every econometrician's tool kit." George Judge, University of California, Berkeley
"Pagan and Ullah have brought together a large set of research results in semi- and nonparametric estimation that greatly improves the accessibility of this important body of research to graduate students and professionals." Joel Horowitz, University of Iowa
"A valuable treatment of nonparametric and semiparametric methods in econometrics. This book will be a useful resource for years to come." Oliver Linton, Yale University
"This book covers an enormous amount of material in a succinct and user friendly fashion.... I strongly recommend it." Professor O.B. Linton, London School of Economics
"...thorough and elegant." Mathematics of Computing
"Pagan and Ullah's textbook, Nonparametric Econometrics, is not intended to be a 'cookbook' nor would it be confused with one. If, however, you are looking for the most comprehensive collection of nonparametirc and semiparametric methods dealing with those issues that are often encountered by applied economists, then this definitely is the book for you. This textbook deserves to be in the library of all economists who wish to keep abreast of less-than-fully parametric econometric techniques, and would serve readily as the cornerstone for a graduate course on the subject." JASA
This books systematically and thoroughly covers a vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the last five decades. Within this framework, this is the first book to discuss the principles of the nonparametric approach to the topics covered in a first year graduate course in econometrics, e.g., regression function, heteroskedasticity, simultaneous equations models, logit-probit and censored models. Professors Pagan and Ullah provide intuitive explanations of difficult concepts, heuristic developments of theory, and empirical examples emphasizing the usefulness of modern nonparametric approach. The book should provide a new perspective on teaching and research in applied subjects in general and econometrics and statistics in particular.
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The authors are adept at introducing the subject, and they use the natural logical progression: first they show how to estimate a density; and then they lead the reader to think of a kernel regression as the conditional moment of a bivariate density.
The author's notation confused me because to estimate a density is really to estimate a function at each of the points in its domain. Sometimes the authors made this point clear and sometimes their arguments obfuscated this subtlety.
A strength seems to be the breadth of the bibliographic references. Another strength seems to be the empirical applications that follow the cursory theoretical discussions.
The book will appeal to a middle-brow reader like myself, but mathematicians and good statisticians may find the analysis a trifle loose.