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Robust penalized quantile regression estimation for panel data. (English) Zbl 1431.62161

Summary: 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.

MSC:

62G08 Nonparametric regression and quantile regression
62J07 Ridge regression; shrinkage estimators (Lasso)
62P20 Applications of statistics to economics

Software:

PDCO; quantreg; SemiPar
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Full Text: DOI

References:

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