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Estimating Linear Probability Functions: A Comparison of Approaches

Published online by Cambridge University Press:  28 April 2015

David L. Debertin ,
Angelos Pagoulatos  and
Eldon D. Smith
David L. Debertin
Affiliation:
Department of Agricultural Economics, University of Kentucky
Angelos Pagoulatos
Affiliation:
Department of Agricultural Economics, University of Kentucky
Eldon D. Smith
Affiliation:
Department of Agricultural Economics, University of Kentucky
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Extract

A linear probability function permits the estimation of the probability of the occurrence or non-occurrence of a discrete event. Nerlove and Press (p. 3–9) outline several statistical problems that arise if such a function is estimated via OLS. In particular, heteroskedasticity inherent in such a regression model leads to inefficient estimates of parameters (Amemiya 1973, Horn and Horn). Moreover, without restrictions on the conventional OLS model, probability estimates lying outside the unit (0–1) interval are possible (Nerlove and Press). Goldberger and Kmenta suggest two approaches for alleviating the heteroskedasticity problems inherent in the OLS regression model. Logit analysis will also alleviate heteroskedasticity problems and ensure that estimated probabilities will lie within the unit interval (Amemiya 1974, Hauck and Donner, Hill and Kau, Horn and Horn, Horn, Horn, and Duncan, Theil 1970).

Type
Research Article
Information
Journal of Agricultural and Applied Economics , Volume 12 , Issue 2 , December 1980 , pp. 65 - 69
Copyright
Copyright © Southern Agricultural Economics Association 1980

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