A simple estimator for quantile panel data models using smoothed quantile regressions

This paper considers panel data models where the idiosyncratic errors are subject to conditonal quantile restrictions. We propose a two-step estimator based on smoothed quantile regressions that is easy to implement. The asymptotic distribution of the estimator is established, and the analytical expression of its asymptotic bias is derived. Building on these results, we show how to make asymptotically valid inference on the basis of both analytical and split-panel jackknife bias corrections. Finite-sample simulations are used to support our theoretical analysis and to illustrate the importance of bias correction in quantile regressions for panel data. Finally, in an empirical application, the proposed method is used to study the growth effects of foreign direct investment.