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摘要翻译：
本文提出了面板数据的非参数核平滑估计来检验截面单元间的异质性程度。我们首先估计每个单元的样本均值、自协方差和自相关，然后应用核平滑计算它们的密度函数。核估计量对带宽的依赖性使得极高阶渐近偏差影响了对截面样本容量(N)和时间序列长度(T)相对大小的要求条件。特别是，它使得关于N和T的条件比在没有核平滑的长面板文献中通常观察到的条件更强和更复杂。我们还考虑了一种分割面板折刀方法来校正偏差和置信区间的构造。经验应用和蒙特卡罗模拟在有限样本中说明了我们的过程。
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英文标题：
《Kernel Estimation for Panel Data with Heterogeneous Dynamics》
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作者：
Ryo Okui and Takahide Yanagi
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最新提交年份：
2019
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分类信息：
一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
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.
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PDF链接：
https://arxiv.org/pdf/1802.08825
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