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Forward-selected panel data approach for program evaluation
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DOI：10.1016/j.jeconom.2021.04.009.png)
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Abstract
En 中文
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) con-structs 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. (c) 2021 Elsevier B.V. All rights reserved.
Keywords:
Aggressive algorithm
Average treatment effect
Counterfactual analysis
Post -selection inference
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