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Removing Specification Errors from the Usual Formulation of Binary Choice Models

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posted on 07.06.2016, 12:20 by P. A. V. B. Swamy, I-Lok Chang, Jatinder S. Mehta, William H. Greene, Stephen G. Hall, George S. Tavlas
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.

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Citation

Econometrics, 2016, 4 (2), 26; doi:10.3390/econometrics4020026

Author affiliation

/Organisation/COLLEGE OF SOCIAL SCIENCES, ARTS AND HUMANITIES/Department of Economics

Version

VoR (Version of Record)

Published in

Econometrics

Publisher

MDPI

issn

2225-1146

Acceptance date

19/05/2016

Copyright date

2016

Available date

07/06/2016

Publisher version

http://www.mdpi.com/2225-1146/4/2/26

Language

en

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