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Smoothly mixing regressions (2006)
| Venue: | Journal of Econometrics |
| Citations: | 44 - 4 self |
BibTeX
@ARTICLE{Geweke06smoothlymixing,
author = {John Geweke and Michael Keane},
title = {Smoothly mixing regressions},
journal = {Journal of Econometrics},
year = {2006}
}
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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 non-Baeysian 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.
Keyphrases
attractive alternative simple multinomial probit model first illustration study quantiles second illustration model serial dependence modest computational cost arch model state probability non-baeysian quantile regression sample likelihood criterion mixing regression paper illustration econometric model posterior distribution conventional bayesian mixture observed covariates mcmc algorithm
