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Regression density estimation using smooth adaptive Gaussian mixtures
Stockholm University, Faculty of Social Sciences, Department of Statistics.
2009 (English)In: Journal of Econometrics, ISSN 0304-4076, E-ISSN 1872-6895, Vol. 153, no 2, 155-173 p.Article in journal (Refereed) Published
Abstract [en]

We model a regression density flexibly so that at each value of the covariates the density is a mixture of normals with the means, variances and mixture probabilities of the components changing smoothly as a function of the covariates. The model extends the existing models in two important ways. First, the components are allowed to be heteroscedastic regressions as the standard model with homoscedastic regressions can give a poor fit to heterciscedastic data, especially when the number of covariates is large. Furthermore, we typically need fewer components, which makes it easier to interpret the model and speeds up the computation. The second main extension is to introduce a novel variable selection prior into all the components of the model. The variable selection prior acts as a self-adjusting mechanism that prevents overfitting and makes it feasible to fit flexible high-dimensional surfaces. We use Bayesian inference and Markov Chain Monte Carlo methods to estimate the model. Simulated and real examples are used to show that the full generality of our model is required to fit a large class of densities, but also that special cases of the general model are interesting models for economic data.

Place, publisher, year, edition, pages
2009. Vol. 153, no 2, 155-173 p.
Keyword [en]
Bayesian inference, Markov chain Monte Carlo, Mixture of experts, Nonparametric estimation, Splines, Value-at-Risk, Variable selection
National Category
Economics and Business
URN: urn:nbn:se:su:diva-59664DOI: 10.1016/j.jeconom.2009.05.004ISI: 000271394500005OAI: diva2:432138
authorCount :3Available from: 2011-08-01 Created: 2011-07-05 Last updated: 2011-08-01Bibliographically approved

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Villani, Mattias
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