A $t$-test for synthetic controls

Victor Chernozhukov, Kaspar Wüthrich and Yinchu Zhu ()

Papers from arXiv.org

Abstract: 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, and valid with stationary and non-stationary data. It demonstrates an excellent small sample performance in application-based simulations and performs well relative to alternative methods. We illustrate the usefulness of the $t$-test by revisiting the effect of carbon taxes on emissions.

Date: 2018-12, Revised 2024-01
New Economics Papers: this item is included in nep-big and nep-ecm
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:1812.10820

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