A $t$-test for synthetic controls

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Date

27 décembre 2018

Type de document
Périmètre
Identifiant
  • 1812.10820
Collection

arXiv

Organisation

Cornell University


Résumé 0

We propose a practical and robust method for making inferences on average treatment effects estimated by synthetic controls. We develop a $K$-fold cross-fitting procedure for bias-correction. To avoid the difficult estimation of the long-run variance, inference is based on a self-normalized $t$-statistic, which has an asymptotically pivotal $t$-distribution. Our $t$-test is easy to implement, provably robust against misspecification, valid with non-stationary data, and demonstrates an excellent small sample performance. Compared to difference-in-differences, our method often yields more than 50% shorter confidence intervals and is robust to violations of parallel trends assumptions. An $\texttt{R}$-package for implementing our methods is available.

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