A simple and trustworthy asymptotic t test in difference-in-differences regressions

Cheng Liu and Yixiao Sun

Journal of Econometrics, 2019, vol. 210, issue 2, 327-362

Abstract: We propose an asymptotically valid t test that uses Student’s t distribution as the reference distribution in a difference-in-differences regression. For the asymptotic variance estimation, we adopt the clustering-by-time approach to accommodate cross-sectional dependence. This approach often assumes the clusters to be independent across time, but we allow them to be temporally dependent. The proposed t test is based on a special heteroscedasticity and autocorrelation robust (HAR) variance estimator. We target the type I and type II errors and develop a testing-oriented method to select the underlying smoothing parameter. By capturing the estimation uncertainty of the HAR variance estimator, the t test has more accurate size than the corresponding normal test and is just as powerful as the latter. Compared to the nonstandard test developed in the literature, the standard t test is just as accurate but much more convenient to use. Model-based and empirical-data-based Monte Carlo simulations show that the t test works quite well in finite samples.

Keywords: Basis functions; Difference-in-differences; Fixed-smoothing asymptotics; Heteroscedasticity and autocorrelation; Student’s t distribution; t test (search for similar items in EconPapers)
JEL-codes: C12 C33 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Working Paper: A Simple and Trustworthy Asymptotic t Test in Difference-in-Differences Regressions (2019)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:210:y:2019:i:2:p:327-362

DOI: 10.1016/j.jeconom.2019.02.003

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