Bayesian econometrics and forecasting

Geweke, J

Bayesian econometrics and forecasting

Geweke, J

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Publication Type:: Journal Article
Citation:: Journal of Econometrics, 2001, 100 (1), pp. 11 - 15
Issue Date:: 2001-01-01

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Field	Value	Language
dc.contributor.author	Geweke, J	en_US
dc.date.issued	2001-01-01	en_US
dc.identifier.citation	Journal of Econometrics, 2001, 100 (1), pp. 11 - 15	en_US
dc.identifier.issn	0304-4076	en_US
dc.identifier.uri	http://hdl.handle.net/10453/13892
dc.description.abstract	Contemporary Bayesian forecasting methods draw on foundations in subjective probability and preferences laid down in the mid-twentieth century, and utilize numerical methods developed since that time in their implementation. These methods unify the tasks of forecasting and model evaluation. They also provide tractable solutions for problems that prove difficult when approached using non-Bayesian methods. These advantages arise from the fact that the conditioning in Bayesian probability forecasting is the same as the conditioning in the underlying decision problems. © 2001 Elsevier Science S.A. All rights reserved.	en_US
dc.relation.ispartof	Journal of Econometrics	en_US
dc.relation.isbasedon	10.1016/S0304-4076(00)00046-4	en_US
dc.subject.classification	Econometrics	en_US
dc.title	Bayesian econometrics and forecasting	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	100	en_US
utslib.for	140303 Economic Models and Forecasting	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1403 Econometrics	en_US
dc.location.activity	Brisbane, Australia
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.issue	1	en_US
pubs.publication-status	Published	en_US
pubs.volume	100	en_US

Abstract:

Contemporary Bayesian forecasting methods draw on foundations in subjective probability and preferences laid down in the mid-twentieth century, and utilize numerical methods developed since that time in their implementation. These methods unify the tasks of forecasting and model evaluation. They also provide tractable solutions for problems that prove difficult when approached using non-Bayesian methods. These advantages arise from the fact that the conditioning in Bayesian probability forecasting is the same as the conditioning in the underlying decision problems. © 2001 Elsevier Science S.A. All rights reserved.

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http://hdl.handle.net/10453/13892