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Linear Probability, Logit, and Probit Models de John H. Aldrich

Linear Probability, Logit, and Probit Models

Autor John H. Aldrich, Forrest D Nelson
en Limba Engleză Paperback – noi 1984
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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Specificații

ISBN-13: 9780803921337
ISBN-10: 0803921330
Pagini: 96
Ilustrații: illustrations, bibliography
Dimensiuni: 140 x 216 x 6 mm
Greutate: 0.13 kg
Ediția:Expanded.
Editura: Sage Publications, Inc
Locul publicării:Thousand Oaks, United States

Cuprins

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

Notă biografică

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.

Descriere

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.