Panel Data Analysis with Heterogeneous Dynamics

TL;DR

This paper introduces a model-free method for analyzing panel data with diverse dynamic behaviors, utilizing empirical distributions and advanced bias correction techniques to improve inference accuracy.

Contribution

It develops a novel approach combining empirical distribution estimation with bias correction and bootstrap inference for heterogeneous panel data analysis.

Findings

Bias correction improves small-sample inference accuracy.

Method effectively captures heterogeneous dynamic structures.

Simulation confirms robustness of the proposed procedures.

Abstract

This paper proposes a model-free approach to analyze panel data with heterogeneous dynamic structures across observational units. We first compute the sample mean, autocovariances, and autocorrelations for each unit, and then estimate the parameters of interest based on their empirical distributions. We then investigate the asymptotic properties of our estimators using double asymptotics and propose split-panel jackknife bias correction and inference based on the cross-sectional bootstrap. We illustrate the usefulness of our procedures by studying the deviation dynamics of the law of one price. Monte Carlo simulations confirm that the proposed bias correction is effective and yields valid inference in small samples.