A BAYESIAN INTERPRETATION OF MULTIPLE POINT ESTIMATES

Mahmoud El-Gamal

Econometric Reviews, 2001, vol. 20, issue 2, 235-245

Abstract: Consider a large number of econometric investigations using different estimation techniques and/or different subsets of all available data to estimate a fixed set of parameters. The resulting empirical distribution of point estimates can be shown - under suitable conditions - to coincide with a Bayesian posterior measure on the parameter space induced by a minimum information procedure. This Bayesian interpretation makes it easier to combine the results of various empirical exercises for statistical decision making. The collection of estimators may be generated by one investigator to ensure the satisfaction of our conditions, or they may be collected from published works, where behavioral assumptions need to be made regarding the dependence structure of econometric studies.

Keywords: Bayesian statistics and econometrics; Decision theory; Literature surveys; Meta-analysis; Markov random fields; Gibbs random fields; Point estimation; JEL Classification: C11; C13; C42; C44; and C51 (search for similar items in EconPapers)
Date: 2001
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DOI: 10.1081/ETC-100103825

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