Optimal prediction pools

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dc.contributor.author Geweke, John en_US
dc.contributor.author Amisano, Gianni en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-10-12T03:34:55Z
dc.date.available 2012-10-12T03:34:55Z
dc.date.issued 2011 en_US
dc.identifier 2011000467 en_US
dc.identifier.citation Geweke John and Amisano Gianni 2011, 'Optimal prediction pools', Elsevier Science Sa, vol. 164, no. 1, pp. 130-141. en_US
dc.identifier.issn 0304-4076 en_US
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/18820
dc.description.abstract We consider the properties of weighted linear combinations of prediction models, or linear pools, evaluated using the log predictive scoring rule. Although exactly one model has limiting posterior probability, an optimal linear combination typically includes several models with positive weights. We derive several interesting results: for example, a model with positive weight in a pool may have zero weight if some other models are deleted from that pool. The results are illustrated using S&P 500 returns with six prediction models. In this example models that are clearly inferior by the usual scoring criteria have positive weights in optimal linear pools. en_US
dc.language en_US
dc.publisher Elsevier Science Sa en_US
dc.relation.isbasedon http://dx.doi.org/10.1016/j.jeconom.2011.02.017 en_US
dc.title Optimal prediction pools en_US
dc.parent Journal Of Econometrics en_US
dc.journal.volume 164 en_US
dc.journal.number 1 en_US
dc.publocation Lausanne en_US
dc.identifier.startpage 130 en_US
dc.identifier.endpage 141 en_US
dc.cauo.name BUS.School of Finance and Economics en_US
dc.conference Verified OK en_US
dc.for 140300 en_US
dc.personcode 101228 en_US
dc.personcode 0000053741 en_US
dc.percentage 100 en_US
dc.classification.name Econometrics 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 Forecasting Log scoring Model combination S&P 500 returns en_US

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