Journal
JOURNAL OF ECONOMETRICS
Volume 157, Issue 2, Pages 396-408Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.jeconom.2010.03.042
Keywords
Shrinkage; Robust; Quantile regression; Panel data; Individual effects
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This paper investigates a class of penalized quantile regression estimators for panel data. The penalty serves to shrink a vector of individual specific effects toward a common value. The degree of this shrinkage is controlled by a tuning parameter X. It is shown that the class of estimators is asymptotically unbiased and Gaussian, when the individual effects are drawn from a class of zero-median distribution functions. The tuning parameter, X. can thus be selected to minimize estimated asymptotic variance. Monte Carlo evidence reveals that the estimator can significantly reduce the variability of the fixed-effect version of the estimator without introducing bias. (C) 2010 Elsevier B.V. All rights reserved.
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