Robust penalized quantile regression estimation for panel data

Carlos Lamarche

Journal of Econometrics, 2010, vol. 157, issue 2, 396-408

Abstract: 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 [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.

Keywords: Shrinkage; Robust; Quantile; regression; Panel; data; Individual; effects (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (177)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:157:y:2010:i:2:p:396-408

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