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.

中文翻译：

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具有异构动态的面板数据的内核估计

本文提出了针对面板数据的非参数核平滑估计，以检验跨横截面单元的异质性程度。我们首先估算每个单元的样本均值，自协方差和自相关，然后应用核平滑来计算其密度函数。核估计器对带宽的依赖性使得极高的渐近偏差会影响所需的条件，这些条件取决于横截面样本大小（N）和时间序列长度（T）的相对大小。特别是，它使N和T上的条件比没有内核平滑的长面板文献中通常观察到的条件更强，更复杂。我们还考虑了分屏折刀方法来校正偏差和置信区间的构建。