MSE-Optimal Difference-in-Differences Estimator
Econometrics
2026-05-07 v1
Abstract
This paper develops a difference-in-differences (DiD) estimation method that selects the optimal length of pre-trends by minimizing the mean squared error (MSE). Conventional DiD regression models, such as the two-way fixed effects model or the event study model, may suffer from accuracy and validity concerns. If the sample size is small, the estimator may have a larger variance. Also, pre-tests often lack power to detect violations of the parallel trends assumption as Roth (2022) highlights. By focusing on the bias and variance tradeoff, the proposed method derives the MSE-optimal estimator from the optimal length of pre-trends. Simulation results and an empirical application demonstrate the practical applicability of the proposed method.
Keywords
Cite
@article{arxiv.2605.05056,
title = {MSE-Optimal Difference-in-Differences Estimator},
author = {Yamato Igarashi},
journal= {arXiv preprint arXiv:2605.05056},
year = {2026}
}