Abstract：We propose an asymptotically valid test that uses Student’s 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 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 test is just as accurate but much more convenient to use. Model-based and empirical-data-based Monte Carlo simulations show that the test works quite well in finite samples.

研究成果：A simple and trustworthy asymptotic t test in difference-in-differences regressions

发表期刊：Journal of Econometrics

论文链接：https://www.sciencedirect.com/science/article/pii/S0304407619300363