Standard Errors for Difference‐in‐Difference Regression

2025/01/22 by Bruce E. Hansen · 1 voice · 1 citation
Mathematics · #Statistical Methods and Inference #Statistical Methods and Bayesian Inference #Advanced Statistical Methods and Models

paper · pdf · doi:10.1002/jae.3110

Abstract

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 influential DiD applications and showing how inferential results can change if jackknife standard error and inference methods are used.

Citations

• Using Heteroscedasticity Consistent Standard Errors in the Linear Regression Model
• A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity
• Longitudinal data analysis using generalized linear models
• How Much Should We Trust Differences-In-Differences Estimates?
• Inference with “Difference in Differences” with a Small Number of Policy Changes
• Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results*
• Bootstrap-Based Improvements for Inference with Clustered Errors

Cited by

• Testing the Significance of the Difference-in-Differences Coefficient via Doubly Randomised Inference

Discussions

• #econsky I take a strange pleasure in there being yet another reason that prior DID papers could be wrong. [That said, failure to reject the null n.e. null is true. Your point estimate is still your [bsky, 32 points, 2 comments]

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