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EFFICIENT SEMIPARAMETRIC SEEMINGLY UNRELATED QUANTILE REGRESSION ESTIMATION

Published online by Cambridge University Press:  01 October 2009

Sung Jae Jun  and
Joris Pinkse
Sung Jae Jun*
Affiliation:
The Pennsylvania State University and The Center for the Study of Auctions, Procurements and Competition Policy
Joris Pinkse
Affiliation:
The Pennsylvania State University and The Center for the Study of Auctions, Procurements and Competition Policy
*
*Address correspondence to Sung Jae Jun, Department of Economics, The Pennsylvania State University, 608 Kern Graduate Building, University Park, PA 16802, U.S.A.; e-mail: [email protected].
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Abstract

We propose an efficient semiparametric estimator for the coefficients of a multivariate linear regression model—with a conditional quantile restriction for each equation—in which the conditional distributions of errors given regressors are unknown. The procedure can be used to estimate multiple conditional quantiles of the same regression relationship. The proposed estimator is asymptotically as efficient as if the true optimal instruments were known. Simulation results suggest that the estimation procedure works well in practice and dominates an equation-by-equation efficiency correction if the errors are dependent conditional on the regressors.

Type
ARTICLES
Information
Econometric Theory , Volume 25 , Issue 5 , October 2009 , pp. 1392 - 1414
Copyright
Copyright © Cambridge University Press 2009

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