A0566
Title: Partitioned wild bootstrap for panel data quantile regression
Authors: Antonio Galvao - Michigan State University (United States)
Carlos Lamarche - University of Kentucky (United States) [presenting]
Thomas Parker - University of Waterloo (Canada)
Abstract: Practical inference procedures for quantile regression models of panel data have been a pervasive concern in empirical work, and can be especially challenging when the panel is observed over many time periods and temporal dependence needs to be taken into account. A new bootstrap method is proposed that applies random weighting to a partition of the data (partition-invariant weights are used in the bootstrap data-generating process) to conduct statistical inference for conditional quantiles in panel data that have significant time-series dependence. It is demonstrated that the procedure is asymptotically valid for approximating the distribution of the fixed effects quantile regression estimator. The bootstrap procedure offers a viable alternative to existing resampling methods. Simulation studies show numerical evidence that the novel approach has accurate small-sample behavior, and an empirical application illustrates its use.
