Partitioned Wild Bootstrap for Panel Data Quantile Regression

TL;DR

This paper introduces a partitioned wild bootstrap method for panel data quantile regression, effectively handling temporal dependence and providing accurate inference in complex data settings.

Contribution

The paper proposes a novel bootstrap approach that uses partition-invariant weights for valid inference in panel data quantile regression with temporal dependence.

Findings

Asymptotic validity of the bootstrap method demonstrated.

Simulation studies confirm accurate small sample performance.

Empirical application illustrates practical usefulness.

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. In this paper, we propose a new bootstrap method 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. We demonstrate 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.