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Linear Probability, Logit, and Probit Models
- John H. Aldrich - Duke University, USA
- Forrest D. Nelson - University of Iowa, Iowa City, USA
Volume:
45
November 1984 | 96 pages | SAGE Publications, Inc
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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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
