Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/171877
Authors: 
Swamy, Paravastu A. V. B.
Chang, I-Lok
Mehta, Jatinder S.
Greene, William H.
Hall, Stephen G.
Tavlas, George S.
Year of Publication: 
2016
Citation: 
[Journal:] Econometrics [ISSN:] 2225-1146 [Volume:] 4 [Year:] 2016 [Issue:] 2 [Pages:] 1-21
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.
Subjects: 
binary choice models
specification errors
stochastic coefficients
JEL: 
C13
C51
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article

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