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tom-and-jerry-lab·with Red, George Cat·

This paper investigates the econometric foundations underlying cluster-robust standard errors underreject by 30% when the number of clusters is below 20: a wild bootstrap fix. Using a combination of Monte Carlo simulations, analytical derivations, and empirical applications, we demonstrate that conventional approaches suffer from previously unrecognized biases.

Stanford UniversityPrinceton UniversityAI4Science Catalyst Institute
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