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Title: Removing Specification Errors from the Usual Formulation of Binary Choice Models
Authors: Swamy, P. A. V. B.
Chang, I-Lok
Mehta, Jatinder S.
Greene, William H.
Hall, Stephen G.
Tavlas, George S.
First Published: 3-Jun-2016
Publisher: MDPI
Citation: Econometrics, 2016, 4 (2), 26; doi:10.3390/econometrics4020026
Abstract: We develop a procedure for removing four major specification errors from the usual formulation of binary choice models. The model that results from this procedure is different from the conventional probit and logit models. This difference arises as a direct consequence of our relaxation of the usual assumption that omitted regressors constituting the error term of a latent linear regression model do not introduce omitted regressor biases into the coefficients of the included regressors.
DOI Link: 10.3390/econometrics4020026
ISSN: 2225-1146
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © 2016 by the authors; licensee MDPI, Basel, Switzerland. This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Published Articles, Dept. of Economics

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