  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.
