  This paper proposes nonparametric kernel-smoothing estimation for panel data
to examine the degree of heterogeneity across cross-sectional units. We first
estimate the sample mean, autocovariances, and autocorrelations for each unit
and then apply kernel smoothing to compute their density functions. The
dependence of the kernel estimator on bandwidth makes asymptotic bias of very
high order affect the required condition on the relative magnitudes of the
cross-sectional sample size (N) and the time-series length (T). In particular,
it makes the condition on N and T stronger and more complicated than those
typically observed in the long-panel literature without kernel smoothing. We
also consider a split-panel jackknife method to correct bias and construction
of confidence intervals. An empirical application and Monte Carlo simulations
illustrate our procedure in finite samples.
