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  . We also consider a split-panel jackknife method to correct bias , construction of confidence intervals , and estimation of cumulative distribution functions based on the density estimation . An empirical application and Monte Carlo simulations illustrate our procedure in finite samples.
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.
A
without kernel smoothing
R
,
and
D
, and estimation of cumulative distribution functions based on the density estimation