Nonparametric Econometrics
1st Edition
Published on 17. February 2011
978-0-511-88949-3 (ISBN)
Description
This book systematically and thoroughly covers the 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. Nonparametric and semiparametric methods potentially offer considerable reward to applied researchers, owing to the methods' ability to adapt to many unknown features of the data. Professors Pagan and Ullah provide intuitive explanations of difficult concepts, heuristic developments of theory, and empirical examples emphasizing the usefulness of the 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.
Reviews / Votes
'The authors of this well-produced volume merit high praise for their endeavours. This will be the most comprehensive summary of nonparametric statistics that we are likely to see for a long time. I can recommend it as a guide to recent work in an important area of mathematical statistics.' Short Book ReviewsMore details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Publishing group
Cambridge University Press
Target group
Professional and scholarly
Illustrations
8 Tables, unspecified; 17 Line drawings, unspecified
File size
3,64 MB
ISBN-13
978-0-511-88949-3 (9780511889493)
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Schweitzer Classification
Other editions
Additional editions
Adrian Pagan | Aman Ullah
Nonparametric Econometrics
Book
06/1999
Cambridge University Press
€150.40
Shipment within 15-20 days
Adrian Pagan | Aman Ullah
Nonparametric Econometrics
Book
06/1999
Cambridge University Press
€72.80
Shipment within 15-20 days
Persons
Content
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.
