Robust Empirical Bayes Confidence Intervals

Timothy Armstrong, Michal Kolesár and Mikkel Plagborg-Møller
Additional contact information
Timothy Armstrong: University of Southern California
Michal Kolesár: Princeton University
Mikkel Plagborg-Møller: Princeton University
Authors registered in the RePEc Author Service: Mikkel Plagborg-Moller

Working Papers from Princeton University. Economics Department.

Abstract: We construct robust empirical Bayes confidence intervals (EBCIs) in a normal means problem. The intervals are centered at the usual linear empirical Bayes estimator, but use a critical value accounting for shrinkage. Parametric EBCIs that assume a normal distribution for the means (Morris, 1983b) may substantially undercover when this assumption is violated. In contrast, our EBCIs control coverage regardless of the means distribution, while remaining close in length to the parametric EBCIs when the means are indeed Gaussian. If the means are treated as fixed, our EBCIs have an average coverage guarantee: the coverage probability is at least 1−α on average across the n EBCIs for each of the means. Our empirical application considers the effects of U.S. neighborhoods on intergenerational mobility.

Keywords: average coverage; empirical Bayes; confidence interval; shrinkage (search for similar items in EconPapers)
JEL-codes: C11 C14 C18 (search for similar items in EconPapers)
Date: 2022-05
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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https://www.princeton.edu/~mkolesar/papers/ebci.pdf

Related works:
Journal Article: Robust Empirical Bayes Confidence Intervals (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:pri:econom:2022-27

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