Forward-Selected Panel Data Approach for Program Evaluation

TL;DR

This paper introduces a forward selection method for choosing control units in panel data policy evaluation, enabling valid inference in high-dimensional settings with many potential controls.

Contribution

It develops a new forward selection approach with theoretical guarantees for panel data analysis in big data environments, addressing control unit selection.

Findings

Valid post-selection inference established

Method extends to high-dimensional control sets

Algorithms are easy to implement and theoretically supported

Abstract

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, Ching and Wan, 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 environment 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.