Inference with dependent data using cluster covariance estimators
Inference with dependent data using cluster covariance estimators
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DOI：
10.1016/j.jeconom.2011.01.007
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发表时间：
2011-12-01
影响因子：
6.3
通讯作者：
Hansen, Christian B.
中科院分区：
文献类型：
--
作者：
Bester, C. Alan;Conley, Timothy G.;Hansen, Christian B.
本文提出了一种针对时间序列、空间和面板数据应用中的相关数据的推理方法。该方法涉及使用聚类协方差矩阵估计器 (CCE) 构建 t 和 Wald 统计量。我们使用一个近似值，将簇/组的数量固定，并且每组的观察数量很大。由此产生的 t 和 Wald 统计量的极限分布是标准 t 和 F 分布，其中组数起着样本大小的作用。使用少量组类似于 Kiefer 和 Vogelsang (2002, 2005) (IN) 的“固定 b”渐近法，用于异方差性和自相关一致推理。我们提供的模拟证据表明该程序大大优于传统的推理程序。 (C) 2011 Elsevier B.V. 保留所有权利。
This paper presents an inference approach for dependent data in time series, spatial, and panel data applications. The method involves constructing t and Wald statistics using a cluster covariance matrix estimator (CCE). We use an approximation that takes the number of clusters/groups as fixed and the number of observations per group to be large. The resulting limiting distributions of the t and Wald statistics are standard t and F distributions where the number of groups plays the role of sample size. Using a small number of groups is analogous to 'fixed-b' asymptotics of Kiefer and Vogelsang (2002, 2005) (IN) for heteroskedasticity and autocorrelation consistent inference. We provide simulation evidence that demonstrates that the procedure substantially outperforms conventional inference procedures. (C) 2011 Elsevier B.V. All rights reserved.