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Limited-Dependent and Qualitative Variables in Econometrics

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ISBN-10: 0521338255

ISBN-13: 9780521338257

Edition: 1986

Authors: G. S. Maddala, Peter Hammond, Alberto Holly, Andrew Chesher, Matthew Jackson

List price: $51.99
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Description:

This book presents the econometric analysis of single-equation and simultaneous-equation models in which the jointly dependent variables can be continuous, categorical, or truncated. Despite the traditional emphasis on continuous variables in econometrics, many of the economic variables encountered in practice are categorical (those for which a suitable category can be found but where no actual measurement exists) or truncated (those that can be observed only in certain ranges). Such variables are involved, for example, in models of occupational choice, choice of tenure in housing, and choice of type of schooling. Models with regulated prices and rationing, and models for program evaluation, also represent areas of application for the techniques presented by the author.
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Book details

List price: $51.99
Copyright year: 1986
Publisher: Cambridge University Press
Publication date: 6/27/1986
Binding: Paperback
Pages: 414
Size: 6.25" wide x 9.25" long x 0.75" tall
Weight: 1.188
Language: English

G. S. Maddala is a University Eminent Professor of Economics at Ohio State University. He attended Andhra University, Bombay University and the University of Chicago. Maddala taught at the University of Florida, the University of Rochester, Stanford University, and Cornell University. He contributed to The Handbook of Econometrics and authored Introduction of Econometrics.

Preface
Introduction
Discrete regression models
Probabilistic-choice models
Discriminant analysis
Multivariate qualitative variables
Censored and truncated regression models
Simultaneous-equations models with truncated and censored variables
Two-stage estimation methods
Models with self-selectivity
Disequilibrium models
Some applications: unions and wages
Appendix: Some results on truncated distributions
Bibliography
Index