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Thursday  14 November 2019  14:00 - 15:30

Inference for the mean

Ulrich Mueller (Princeton University)

32L 3.05, 3rd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH


STICERD Econometrics Seminar Series

Consider inference about the mean of a population with finite variance, based on an i.i.d. sample. The usual t-statistic yields correct inference in large samples, but heavy tails induce poor small sample behavior. This paper combines extreme value theory for the smallest and largest observations with a normal approximation for the t-statistic of a truncated sample to obtain more accurate inference. This alternative approximation is shown to provide a refinement over the standard normal approximation to the full sample t-statistic under more than two but less than three moments, while the bootstrap does not. Small sample simulations suggest substantial size improvements over the bootstrap, also in an application to linear regression inference with clustered standard errors