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AN INTEGRATED KERNEL-WEIGHTED SMOOTHED MAXIMUM SCORE ESTIMATOR FOR THE PARTIALLY LINEAR BINARY RESPONSE MODEL

Published online by Cambridge University Press:  29 November 2013

Jerome M. Krief
Jerome M. Krief*
Affiliation:
University of Virginia
*
*Address correspondence to Jerome Krief, University of Virginia, Department of Economics, Charlottesville, VA 22904.
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Abstract

This paper considers a binary response model with a partially linear latent equation, where ϕ is an unknown function and β is a finite-dimensional parameter of interest. Using the principle of smoothed maximum score estimation (Horowitz, 1992; Econometrica 60(3), 505–531), a consistent and asymptotically normal (C.A.N.)estimator for β is proposed under the restriction that the median of the error conditional on the covariates is equal to 0. Furthermore, the rate of convergence in probability is close to the parametric rate, if certain functions admit enough derivatives. This method neither restricts the form of heteroskedasticity in the error term nor suffers from the curse of dimensionality whenever ϕ is multivariate. Some Monte Carlo experiments suggest that this estimator performs well compared with conventional estimators.

Type
ARTICLES
Information
Econometric Theory , Volume 30 , Issue 3 , June 2014 , pp. 647 - 675
Copyright
Copyright © Cambridge University Press 2013 

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