Smoothly mixing regressions

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dc.contributor.author Geweke, John en_US
dc.contributor.author Keane, Michael en_US
dc.contributor.editor en_US
dc.date.accessioned 2010-05-28T09:53:01Z
dc.date.available 2010-05-28T09:53:01Z
dc.date.issued 2007 en_US
dc.identifier 2006008135 en_US
dc.identifier.citation Geweke John and Keane Michael 2007, 'Smoothly mixing regressions', Elsevier Science Publishers B.V., vol. 138, no. 1, pp. 252-290. en_US
dc.identifier.issn 0304-4076 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/9944
dc.description.abstract This paper extends the conventional Bayesian mixture of normals model by permitting state probabilities to depend on observed covariates. The dependence is captured by a simple multinomial probit model. A conventional and rapidly mixing MCMC algorithm provides access to the posterior distribution at modest computational cost. This model is competitive with existing econometric models, as documented in the paper's illustrations. The first illustration studies quantiles of the distribution of earnings of men conditional on age and education, and shows that smoothly mixing regressions are an attractive alternative to nonBayesian quantile regression. The second illustration models serial dependence in the S&P 500 return, and shows that the model compares favorably with ARCH models using out of sample likelihood criteria. en_US
dc.language en_US
dc.publisher Elsevier Science Publishers B.V. en_US
dc.relation.isbasedon http://dx.doi.org/10.1016/j.jeconom.2006.05.022 en_US
dc.title Smoothly mixing regressions en_US
dc.parent Journal of Econometrics en_US
dc.journal.volume 138 en_US
dc.journal.number 1 en_US
dc.publocation Amsterdam, The Netherlands en_US
dc.identifier.startpage 252 en_US
dc.identifier.endpage 290 en_US
dc.cauo.name BUS.School of Finance and Economics en_US
dc.conference Verified OK en_US
dc.for 140302 en_US
dc.personcode 101228 en_US
dc.personcode 998871 en_US
dc.percentage 100 en_US
dc.classification.name Econometric and Statistical Methods 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 998871 en_US


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