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.

Giuseppe De Luca, Jan R Magnus, Franco Peracchi (2022). Sampling properties of the Bayesian posterior mean with an application to WALS estimation. JOURNAL OF ECONOMETRICS, 299-317 [10.1016/j.jeconom.2021.04.008].

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

Giuseppe De Luca
Membro del Collaboration Group
;
2022-01-01

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.
2022
Settore SECS-P/05 - Econometria
Giuseppe De Luca, Jan R Magnus, Franco Peracchi (2022). Sampling properties of the Bayesian posterior mean with an application to WALS estimation. JOURNAL OF ECONOMETRICS, 299-317 [10.1016/j.jeconom.2021.04.008].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/512646
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