We analyze the conditions under which the Synthetic Control (SC) estimator is asymptotically unbiased when the number of pre-treatment periods goes to infinity. We show that the SC estimator is generally asymptotically biased if treatment assignment is correlated with time-varying unobserved...

We evaluate whether a lack of guidance on how to choose the matching variables used in the Synthetic Control (SC) estimator creates specification-searching opportunities. We first provide theoretical results showing that specification-searching opportunities would be asymptotically irrelevant...

We analyze the conditions under which the Synthetic Control (SC) estimator is unbiased. We show that the SC estimator is generally biased if treatment assignment is correlated with time-varying unobserved confounders, even when the number of pre-treatment periods goes to infinity and in settings...

We show that existing inference methods used in Differences-in-Differences might not perform well with few treated groups and heteroskedastic errors. This is restrictive because variation in the number of observations per group inherently leads to heteroskedasticity in the group x time aggregate...

We evaluate whether a lack of guidance on how to choose the matching variables used in the Synthetic Control (SC) estimator creates specification-searching opportunities. We first provide theoretical results showing that specification-searching opportunities would be asymptotically irrelevant...

We analyze the conditions under which the Synthetic Control (SC) estimator is unbiased. We show that the SC estimator is generally biased if treatment assignment is correlated with unobserved confounders, even when the number of pre-treatment periods goes to infinity, and in settings where one...

We analyze the properties of the Synthetic Control (SC) and related estimators when the pre-treatment fit is imperfect. In this framework, we show that these estimators are generally biased if treatment assignment is correlated with unobserved confounders, even when the number of pre-treatment...

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, inference in DID models when there are few treated groups is still an open question. We show that usual inference methods used in DID models might not perform well when there...

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, inference in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform well when...

Differences-in-Differences (DID) is one of the most widely used identification strategies in applied economics. However, how to draw inferences in DID models when there are few treated groups remains an open question. We show that the usual inference methods used in DID models might not perform...