CENTRAL LIMIT THEORY FOR COMBINED CROSS SECTION AND TIME SERIES WITH AN APPLICATION TO AGGREGATE PRODUCTIVITY SHOCKS
发展了一个中心极限理论，明确考虑截面与时间序列数据之间的可能依赖关系，并聚焦于共同因子作为依赖机制，为存在总体冲击时两类数据联合使用的参数估计渐近性质提供了理论基础。
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.