Policy evaluation is central to economic data analysis, but economists mostly work with observational data in view of limited opportunities to carry out controlled experiments. In the potential outcome framework, the panel data approach (Hsiao et al., 2012) constructs the counterfactual by exploiting the correlation between cross-sectional units in panel data. The choice of cross-sectional control units, a key step in its implementation, is nevertheless unresolved in data-rich environments when many possible controls are at the researcher’s disposal. We propose the forward selection method to choose control units, and establish validity of the post-selection inference. Our asymptotic framework allows the number of possible controls to grow much faster than the time dimension. The easy-to-implement algorithms and their theoretical guarantee extend the panel data approach to big data settings.

政策评估是经济数据分析的核心，但鉴于进行对照实验的机会有限，经济学家主要使用观察数据。在潜在结果框架中，面板数据方法 (Hsiao et al., 2012) 通过利用面板数据中横截面单位之间的相关性来构建反事实。横截面控制单元的选择是其实施的关键步骤，但在研究人员可以使用许多可能的控件时，在数据丰富的环境中仍未解决。我们提出前向选择方法来选择控制单元，并建立后选择推理的有效性。我们的渐近框架允许可能的控件数量比时间维度增长得更快。