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MOVED TO KGH L070

Carlos Lamarche (University of Kentucky): Wild Bootstrap Inference for Panel Data Quantile Regression with Dependent Data with Antonio F. Galvao (Michigan State University) and Thomas Parker (University of Waterloo)

Abstract: Practical inference procedures for panel data quantile regression have been a pervasive concern in empirical work, and can be especially challenging when the panel is observed over many time periods and dependence needs to be taken into account. Quantile regression estimators have complex limiting distributions that are difficult to approximate. In this paper, we propose a new resampling method to conduct statistical inference for conditional quantiles in panels with observations that exhibit time series dependence. We demonstrate that the procedure is asymptotically valid for approximating the distribution of the panel quantile regression estimator. This wild bootstrap procedure offers a viable alternative to existing bootstrap methods for time series data that can be adapted to panel quantile regression. Simulation studies show that the novel approach has accurate small sample behavior, and an empirical application illustrates its use.

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