How to Accurately Test Significance with Difference in Difference Models

This post introduces and evaluates three methods (averaging, clustering, and permutation testing) for accurately estimating significance in difference-in-differences (DID) models when dealing with autocorrelated time series data. It highlights the limitations of standard OLS significance tests in such scenarios and provides simulation-based evidence on the trade-offs between power and false positives for each method, recommending permutation testing for smaller datasets and clustered standard errors for larger ones.