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Efficient Semiparametric Seemingly Unrelated Quantile Regression Estimation

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Author Info
Jun, Sung Jae
Pinkse, Joris

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Abstract

We propose an efficient semiparametric estimator for the coefficients of a multivariate linear regression model 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.

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File URL: http://journals.cambridge.org/abstract_S0266466609090549
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Publisher Info
Article provided by Cambridge University Press in its journal Econometric Theory.

Volume (Year): 25 (2009)
Issue (Month): 05 (October)
Pages: 1392-1414
Download reference. The following formats are available: HTML (with abstract), plain text (with abstract), BibTeX, RIS (EndNote, RefMan, ProCite), ReDIF
Handle: RePEc:cup:etheor:v:25:y:2009:i:05:p:1392-1414_09

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This page was last updated on 2009-11-24.


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