Linear Probability, Logit, and Probit Models

Linear Probability, Logit, and Probit Models

by John H. Aldrich, Forrest D. Nelson

95 pages· 1984· ISBN 9780803921337
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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.

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