Sampling properties of the Bayesian posterior mean with an application to WALS estimation

Giuseppe De Luca (), Jan R. Magnus and Franco Peracchi

Journal of Econometrics, 2022, vol. 230, issue 2, 299-317

Abstract: Many statistical and econometric learning methods rely on Bayesian ideas. When applied in a frequentist setting, their precision is often assessed using the posterior variance. This is permissible asymptotically, but not necessarily in finite samples. We explore this issue focusing on weighted-average least squares (WALS), a Bayesian-frequentist ‘fusion’. Exploiting the sampling properties of the posterior mean in the normal location model, we derive estimators of the finite-sample bias and variance of WALS. We study the performance of the proposed estimators in an empirical application and a closely related Monte Carlo experiment which analyze the impact of legalized abortion on crime.

Keywords: Normal location model; Posterior moments and cumulants; Double-shrinkage estimators; WALS (search for similar items in EconPapers)
JEL-codes: C11 C13 C15 C52 I21 (search for similar items in EconPapers)
Date: 2022
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Related works:
Working Paper: Sampling properties of the Bayesian posterior mean with anapplication to WALS estimation (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:230:y:2022:i:2:p:299-317

DOI: 10.1016/j.jeconom.2021.04.008

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