Linear Probability, Logit and Probit Models (Quantitative Applications in the Social Sciences 45)
By: John H. Aldrich (author), Forrest D. Nelson (author)Paperback
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Ordinary regression analysis is not appropriate for investigating dichotomous or otherwise 'limited' dependent variables, but this volume examines three techniques -- linear probability, probit, and logit models -- which are well-suited for such data. It reviews the linear probability model and discusses alternative specifications of non-linear models. Using detailed examples, Aldrich and Nelson point out the differences among linear, logit, and probit models, and explain the assumptions associated with each.
John H. Aldrich is Pfizer-Pratt University Professor of Political Science at Duke University. He is author of Why Parties: A Second Look (2011), coeditor of Positive Changes in Political Science (2007), and author of Why Parties (1995) and Before the Convention (1980). He is a past president of both the Southern Political Science Association and the Midwest Political Science Association and is serving as president of the American Political Science Association. In 2001 he was elected a fellow in the American Academy of Arts and Sciences. Expertise * Prediction Markets * Qualitative and Limited Dependent Variable
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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- ID: 9780803921337
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