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ESTIMATION OF BINARY CHOICE MODELS WITH LINEAR INDEX AND DUMMY ENDOGENOUS VARIABLES

Published online by Cambridge University Press:  28 March 2013

Neşe Yildiz
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Abstract

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This paper presents computationally simple estimators for the index coefficients in a binary choice model with a binary endogenous regressor without relying on distributional assumptions or on large support conditions and yields root-n consistent and asymptotically normal estimators. We develop a multistep method for estimating the parameters in a triangular, linear index, threshold-crossing model with two equations. Such an econometric model might be used in testing for moral hazard while allowing for asymmetric information in insurance markets. In outlining this new estimation method two contributions are made. The first one is proposing a novel “matching” estimator for the coefficient on the binary endogenous variable in the outcome equation. Second, in order to establish the asymptotic properties of the proposed estimators for the coefficients of the exogenous regressors in the outcome equation, the results of Powell, Stock, and Stoker (1989, Econometrica 75, 1403–1430) are extended to cover the case where the average derivative estimation requires a first-step semiparametric procedure.

Information

Type
Articles
Information
Econometric Theory , Volume 29 , Issue 2 , April 2013, pp. 354 - 392
Copyright
Copyright © Cambridge University Press 2013 

Footnotes

I would like to thank Takeshi Amemiya, Aprajit Mahajan, Edward Vytlacil, James Powell, and Todd Elder for very helpful comments and questions on this paper.

References

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