Trusting difference-in-difference estimates more: An approximate permutation test

Researchers use difference-in-differences models to evaluate the causal effects of policy changes. Because the empirical correlation across firms and time can be ambiguous, estimating consistent standard errors is difficult, and statistical inferences may be biased. I apply an approximate permutation test using simulated interventions to reveal the empirical error distribution of estimated policy effects. In contrast to existing econometric corrections, such as single or double clustering, this approach does not impose a specific parametric form on the residuals. In comparison with alternative parametric tests, this procedure maintains correct size with simulated and real-world interventions. Simultaneously, it improves power.