Using Simulation Methods for Bayesian Econometric Models: Inference, Development and Communication

Geweke, J

Using Simulation Methods for Bayesian Econometric Models: Inference, Development and Communication

Geweke, J

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Publisher:: Taylor & Francis
Publication Type:: Journal Article
Citation:: Econometric Reviews, 1999, 18 (1), pp. 1 - 73
Issue Date:: 1999-01

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Field	Value	Language
dc.contributor.author	Geweke, J	en_US
dc.date.issued	1999-01	en_US
dc.identifier.citation	Econometric Reviews, 1999, 18 (1), pp. 1 - 73	en_US
dc.identifier.issn	0747-4938	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13922
dc.description.abstract	Abstract: This paper surveys the fundamental principles of subjective Bayesian inference in econometrics and the implementation of those principles using posterior simulation methods. The emphasis is on the combination of models and the development of predictive distributions. Moving beyond conditioning on a fixed number of completely specified models, the paper introduces subjective Bayesian tools for formal comparison of these models with as yet incompletely specified models. The paper then shows how posterior simulators can facilitate communication between investigators (for example, econometricians) on the one hand and remote clients (for example, decision makers) on the other, enabling clients to vary the prior distributions and functions of interest employed by investigators. A theme of the paper is the practicality of subjective Bayesian methods. To this end, the paper describes publicly available software for Bayesian inference, model development, and communication and provides illustrations using two simple econometric models.	en_US
dc.publisher	Taylor & Francis	en_US
dc.relation.ispartof	Econometric Reviews	en_US
dc.subject.classification	Econometrics	en_US
dc.title	Using Simulation Methods for Bayesian Econometric Models: Inference, Development and Communication	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	18	en_US
utslib.for	140302 Econometric and Statistical Methods	en_US
utslib.for	1403 Econometrics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics Discipline
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.issue	1	en_US
pubs.volume	18	en_US

Abstract:

Abstract: This paper surveys the fundamental principles of subjective Bayesian inference in econometrics and the implementation of those principles using posterior simulation methods. The emphasis is on the combination of models and the development of predictive distributions. Moving beyond conditioning on a fixed number of completely specified models, the paper introduces subjective Bayesian tools for formal comparison of these models with as yet incompletely specified models. The paper then shows how posterior simulators can facilitate communication between investigators (for example, econometricians) on the one hand and remote clients (for example, decision makers) on the other, enabling clients to vary the prior distributions and functions of interest employed by investigators. A theme of the paper is the practicality of subjective Bayesian methods. To this end, the paper describes publicly available software for Bayesian inference, model development, and communication and provides illustrations using two simple econometric models.

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http://hdl.handle.net/10453/13922