Smoothly mixing regressions

Geweke, J; Keane, M

Smoothly mixing regressions

Geweke, J Keane, M

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Publication Type:: Journal Article
Citation:: Journal of Econometrics, 2007, 138 (1), pp. 252 - 290
Issue Date:: 2007-05-01

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Field	Value	Language
dc.contributor.author	Geweke, J	en_US
dc.contributor.author	Keane, M	en_US
dc.date.issued	2007-05-01	en_US
dc.identifier.citation	Journal of Econometrics, 2007, 138 (1), pp. 252 - 290	en_US
dc.identifier.issn	0304-4076	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. © 2006 Elsevier B.V. All rights reserved.	en_US
dc.relation.ispartof	Journal of Econometrics	en_US
dc.relation.isbasedon	10.1016/j.jeconom.2006.05.022	en_US
dc.subject.classification	Econometrics	en_US
dc.title	Smoothly mixing regressions	en_US
dc.type	Journal Article
utslib.citation.volume	1	en_US
utslib.citation.volume	138	en_US
utslib.for	140302 Econometric and Statistical Methods	en_US
utslib.for	0104 Statistics	en_US
utslib.for	1402 Applied Economics	en_US
utslib.for	1403 Econometrics	en_US
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	138	en_US

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. © 2006 Elsevier B.V. All rights reserved.

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http://hdl.handle.net/10453/9944