Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data
提出在时间维度较大时，扩展非参数协方差矩阵估计方法，使标准误估计对空间和时间依赖具有稳健性，并通过蒙特卡洛模拟和实证例子说明其有效性。
Many panel data sets encountered in macroeconomics, international economics, regional science, and finance are characterized by cross-sectional or "spatial" dependence. Standard techniques that fail to account for this dependence will result in inconsistently estimated standard errors. In this paper we present conditions under which a simple extension of common nonparametric covariance matrix estimation techniques yields standard error estimates that are robust to very general forms of spatial and temporal dependence as the time dimension becomes large. We illustrate the relevance of this approach using Monte Carlo simulations and a number of empirical examples. © 1998 by the President and Fellows of Harvard College and the Massachusetts Institute of Technolog