Robust penalized quantile regression estimation for panel data
AbstractThis 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 [lambda]. 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, [lambda], 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.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Econometrics.
Volume (Year): 157 (2010)
Issue (Month): 2 (August)
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Web page: http://www.elsevier.com/locate/jeconom
Shrinkage Robust Quantile regression Panel data Individual effects;
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