Randomization inference for difference-in-differences with few treated clusters

Randomization Inference for Differences-in- Differences with Few Treated Clusters

Inference using di erence-in-di erences with clustered data requires care. Previous research has shown that, when there are few treated clusters, t tests based on a clusterrobust variance estimator (CRVE) severely over-reject, di erent variants of the wild cluster bootstrap can over-reject or under-reject dramatically, and procedures based on randomization inference show promise. We demonstrate...

Inference with Few Heterogeneous Clusters∗

Suppose estimating a model on each of a small number of potentially heterogeneous clusters yields approximately independent, unbiased and Gaussian parameter estimators. We make two contributions in this set-up. First, we show how to compare a ∗The authors would like to thank three anonymous referees and seminar and conference participants at various universities and conferences for helpful comm...

Inference with Few Heterogenous Clusters

Consider inference with a small number of potentially heterogeneous clusters. Suppose estimating the model on each cluster yields q asymptotically unbiased, independent Gaussian estimators with potentially heterogeneous variances. Following Ibragimov and Müller (2010), one can then conduct asymptotically valid inference with a standard t-test based on the q cluster estimators, since at conventi...

The Impact of Damage Caps on Malpractice Claims: Randomization Inference with Difference-in-Differences

Inference with Difference in Differences and Other Panel Data∗

We examine inference in panel data when the number of groups is small as is typically the case for differences-in-differences estimation and when some variables are fixed within groups. In this case, standard asymptotics based on the number of groups going to infinity provide a poor approximation to the finite sample distribution. We show that in some cases the t-statistic is distributed as t a...