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

Michael Zimmert

Papers from arXiv.org

Abstract: This study considers various semiparametric difference-in-differences models under different assumptions on the relation between the treatment group identifier, time and covariates for cross-sectional and panel data. The variance lower bound is shown to be sensitive to the model assumptions imposed implying a robustness-efficiency trade-off. The obtained efficient influence functions lead to estimators that are rate double robust and have desirable asymptotic properties under weak first stage convergence conditions. This enables to use sophisticated machine-learning algorithms that can cope with settings where common trend confounding is high-dimensional. The usefulness of the proposed estimators is assessed in an empirical example. It is shown that the efficiency-robustness trade-offs and the choice of first stage predictors can lead to divergent empirical results in practice.

Date: 2018-09, Revised 2020-08
New Economics Papers: this item is included in nep-big and nep-ecm
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