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双重差分回归的标准误AI 解读

ABSTRACT This paper makes a case for the use of jackknife methods for standard error, value, and confidence interval construction for difference‐in‐difference (DiD) regression. We review cluster‐robust, bootstrap, and jackknife standard error methods and show that standard methods can substantially underperform in conventional settings. In contrast, our proposed jackknife inference methods work well in broad contexts. We illustrate the relevance by replicating several…

时间效应存在序列相关时的双向聚类标准误AI 解读

Abstract We propose improved standard errors and an asymptotic theory for two-way clustered panels. Our theory allow for arbitrary serial dependence in the common time effects, which is excluded by existing two-way methods. Our asymptotic distribution theory is the first which allows for this level of inter-dependence. Under weak conditions, we demonstrate that OLS is asymptotically normal, our proposed variance estimator is consistent, and t-ratios are asymptotically…

非动态面板中的门槛效应：估计、检验与推断AI 解读

Threshold regression methods are developed for non-dynamic panels with individual-specific fixed effects. Least squares estimation of the threshold and regression slopes is proposed using fixed-effects transformations. A non-standard asymptotic theory of inference is developed which allows construction of confidence intervals and testing of hypotheses. The methods are applied to a 15-year sample of 565 US firms to test whether financial constraints affect investment…