Bayesian forecasting

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dc.contributor.author Geweke, John en_US
dc.contributor.author Whiteman, C. en_US
dc.contributor.editor Elliot, G; Granger, CWJ; Timmerman, A en_US
dc.date.accessioned 2012-02-23T00:37:09Z
dc.date.available 2012-02-23T00:37:09Z
dc.date.issued 2006 en_US
dc.identifier 2008008246 en_US
dc.identifier.citation Geweke John and Whiteman C. 2006, 'Bayesian forecasting', in http://dx.doi.org/10.1016/S1574-0706(05)01001-3 (ed.), Elsevier, The Netherlands, pp. 3-80. en_US
dc.identifier.issn 978-0-444-51395-3 en_US
dc.identifier.other B1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/17314
dc.description.abstract Bayesian forecasting is a natural product of a Bayesian approach to inference. The Bayesian approach in general requires explicit formulation of a model, and conditioning on known quantities, in order to draw inferences about unknown ones. In Bayesian forecasting, one simply takes a subset of the unknown quantities to be future values of some variables of interest. This chapter presents the principles of Bayesian forecasting, and describes recent advances in computational capabilities for applying them that have dramatically expanded the scope of applicability of the Bayesian approach. It describes historical developments and the analytic compromises that were necessary prior to recent developments, the application of the new procedures in a variety of examples, and reports on two long-term Bayesian forecasting exercises en_US
dc.language en_US
dc.publisher Elsevier en_US
dc.relation.isbasedon http://dx.doi.org/10.1016/S1574-0706(05)01001-3 en_US
dc.title Bayesian forecasting en_US
dc.parent Handbook of Economic Forecasting en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation The Netherlands en_US
dc.identifier.startpage 3 en_US
dc.identifier.endpage 80 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.personcode 0000053746 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 1 en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Markov chain Monte Carlo; predictive distribution; probability forecasting; simulation; vector autoregression en_US


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