Smoothly Mixing Regressions

Joint with: Michael Keane, Yale

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.

For more information, contact Vee Roberson.