Nonparametric Econometrics / Edition 1

Nonparametric Econometrics / Edition 1

by Adrian Pagan, Aman Ullah
     
 

ISBN-10: 0521586119

ISBN-13: 9780521586115

Pub. Date: 02/28/2006

Publisher: Cambridge University Press

This book systematically and thoroughly covers a vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the past 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

Overview

This book systematically and thoroughly covers a vast literature on the nonparametric and semiparametric statistics and econometrics that has evolved over the past 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.

Product Details

ISBN-13:
9780521586115
Publisher:
Cambridge University Press
Publication date:
02/28/2006
Series:
Themes in Modern Econometrics Series
Edition description:
New Edition
Pages:
424
Sales rank:
1,230,622
Product dimensions:
5.98(w) x 8.98(h) x 0.98(d)

Table of Contents

1. Introduction; 2. Methods of density estimation; 3. Conditional moment estimation; 4. Nonparametric estimation of derivatives; 5. Semiparametric estimation of single equation models; 6. Semi and nonparametric estimation of simultaneous equation models; 7. Semiparametric estimation of discrete choice models; 8. Semiparametric estimation of selectivity models; 9. Semiparametric estimation of censored regression models; 10. Retrospect and prospect.

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