Linear Probability, Logit, and Probit Models

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John H. Aldrich - Duke University, USA
Forrest D. Nelson - University of Iowa, Iowa City, USA

Volume: 45

Series:
Quantitative Applications in the Social Sciences

November 1984 | 96 pages | SAGE Publications, Inc

Description
Contents

After showing why ordinary regression analysis is not appropriate in investigating dichotomous or otherwise "limited" dependent variables, this volume examines three techniques-linear probability, probit, and logit models-well-suited for such data. It reviews the linear probability model and discusses alternative specifications of nonlinear models.

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ISBN: 9780803921337 Paperback Suggested Retail Price: $46.00 Bookstore Price: $36.80

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The Linear Probability Model

Specification of Nonlinear Probability Models

Estimation of Probit and Logit Models for Dichotomous Dependent Variables

Minimum Chi-Square Estimation and Polytomous Models Summary and Extensions

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