Bayesian Econometrics and Forecasting

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dc.contributor.author Geweke, John en_US
dc.contributor.editor en_US
dc.date.accessioned 2011-02-07T06:25:46Z
dc.date.available 2011-02-07T06:25:46Z
dc.date.issued 2001 en_US
dc.identifier 2008008202 en_US
dc.identifier.citation Geweke John 2001, 'Bayesian Econometrics and Forecasting', Elsevier Science Publishers B.V., vol. 100, no. 1, pp. 11-15. en_US
dc.identifier.issn 0304-4076 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/13892
dc.description.abstract 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. en_US
dc.language en_US
dc.publisher Elsevier Science Publishers B.V. en_US
dc.relation.isbasedon http://dx.doi.org/10.1016/S0304-4076(00)00046-4 en_US
dc.title Bayesian Econometrics and Forecasting en_US
dc.parent Journal of Econometrics en_US
dc.journal.volume 100 en_US
dc.journal.number 1 en_US
dc.publocation Amsterdam en_US
dc.identifier.startpage 11 en_US
dc.identifier.endpage 15 en_US
dc.cauo.name BUS.Faculty of Business en_US
dc.conference Verified OK en_US
dc.for 140303 en_US
dc.personcode 101228 en_US
dc.percentage 100 en_US
dc.classification.name Economic Models and Forecasting en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US
dc.staffid 101228 en_US


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