CENTRAL LIMIT THEORY FOR COMBINED CROSS SECTION AND TIME SERIES WITH AN APPLICATION TO AGGREGATE PRODUCTIVITY SHOCKS
CENTRAL LIMIT THEORY FOR COMBINED CROSS SECTION AND TIME SERIES WITH AN APPLICATION TO AGGREGATE PRODUCTIVITY SHOCKS
复制标题
组合截面和时间序列的中心极限理论及其在聚合生产率冲击中的应用
DOI：
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发表时间：
2016
影响因子：
0.8
通讯作者：
Maurizio Mazzocco
中科院分区：
文献类型：
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作者：
J. Hahn;G. Kuersteiner;Maurizio Mazzocco
结合横断面和时间序列数据是实证经济学中一个长期而完善的实践。我们开发了一个中心极限理论，明确地说明了两个数据集之间可能的依赖性。我们将重点放在作为这种依赖背后机制的共同因素上。利用我们的中心极限定理（CLT），我们基于横截面和时间序列数据的组合建立了一类一般模型的参数估计的渐近性质，认识到在总体冲击存在下两个数据源之间的相互依赖性。尽管制定联合CLT所需的分析具有复杂的性质，但由于我们的近似与Murphy和Topel （1985, Journal of Business and Economic Statistics 3,370 - 379）公式的形式相似，实现所得到的参数限制分布是很简单的。
Combining cross-sectional and time-series data is a long and well-established practice in empirical economics. We develop a central limit theory that explicitly accounts for possible dependence between the two datasets. We focus on common factors as the mechanism behind this dependence. Using our central limit theorem (CLT), we establish the asymptotic properties of parameter estimates of a general class of models based on a combination of cross-sectional and time-series data, recognizing the interdependence between the two data sources in the presence of aggregate shocks. Despite the complicated nature of the analysis required to formulate the joint CLT, it is straightforward to implement the resulting parameter limiting distributions due to a formal similarity of our approximations with Murphy and Topel’s (1985, Journal of Business and Economic Statistics 3, 370–379) formula.
影响因子：
6.3
作者：
Kuersteiner GM;Prucha IR
通讯作者：
Prucha IR