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Efficient Difference-in-Differences Estimation with High-Dimensional Common Trend Confounding

Michael Zimmert

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Abstract: In this study, we consider difference-in-differences models with a common trend assumption that is only valid after conditioning on covariates. We suggest novel two-step estimators that allow the covariates to enter the model in a very flexible form. In particular, the first stages can be estimated using supervised machine learning methods. We derive asymptotic results for new semiparametric and linear model based estimators for repeated cross-sections and panel data and show that they have desirable statistical properties like asymptotic normality and double robustness. Further, we establish semiparametric efficiency bounds for difference-in-differences estimation with cross-sectional and panel data. The proposed semiparametric estimators attain the bounds. The usability of the methods is assessed by replicating a study on an employment protection reform. We demonstrate that the notion of high-dimensional common trend confounding has implications for the economic interpretation of the policy evaluation results. Notably, measured reform effects are substantially decreased or even reversed when covariates are included in a data-driven manner.

New Economics Papers: this item is included in nep-big and nep-ecm
Date: 2018-09, Revised 2019-07
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