Bayesian analysis in econometrics
type: Item
instance of: scholarly article
title: Bayesian analysis in econometrics
zbMATH Open document ID: 0685.62090
DOI: 10.1016/0304-4076(88)90072-3
author: Arnold Zellner
published in: Journal of Econometrics
publication date: 1988-01-01T00:00:00Z
review text: Five basic propositions on Bayesian analysis in econometrics are put forward, discussed and illustrated through applications in Bayesian estimation, prediction, control and decision processes. The five propositions deal with the unity of science principle, the Jeffreys- Wrinch simplicity postulate, the prediction principle, a subjective concept of probability and the learning model embedded in Bayes' theorem on prior and posterior probabilities. For applied econometric problems, the following applications are of particular importance: (a) estimation of Stein's n-means, (b) estimation using an asymmetric loss function and (c) control problems viewed as regression processes.
Mathematics Subject Classification ID: 62P20
Mathematics Subject Classification ID: 62A01
Mathematics Subject Classification ID: 62F15
zbMATH DE Number: 4123125
zbMATH Keywords: unity of science principle
zbMATH Keywords: Jeffreys-Wrinch simplicity postulate
zbMATH Keywords: prediction principle
zbMATH Keywords: subjective concept of probability
zbMATH Keywords: learning model
zbMATH Keywords: prior and posterior probabilities
zbMATH Keywords: estimation of Stein's n-means
zbMATH Keywords: asymmetric loss function
zbMATH Keywords: control problems
zbMATH Keywords: regression processes
MaRDI profile type: Publication
full work available at URL: 0304-4076(88)90072-3
OpenAlex ID: W2057118172
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