Scandinavian Working Papers in Economics
Working Paper Series,
Sveriges Riksbank (Central Bank of Sweden)
No 245: Modeling Conditional Densities Using Finite Smooth Mixtures
Feng Li (), Mattias Villani () and Robert Kohn ()
Additional contact information
Feng Li: Department of Statistics, Postal: Stockholm University, 106 91 Stockholm, Sweden,
Mattias Villani: Research Department, Central Bank of Sweden, Postal: Sveriges Riksbank, SE-103 37 Stockholm, Sweden
Robert Kohn: The University of New South Wales, Postal: Australian School of Business Building , UNSW Sydney NSW 2052 , Australia
Abstract: Smooth mixtures, i.e. mixture models with covariate-dependent mixing weights, are very useful flexible models for conditional densities. Previous work shows that using too simple mixture components for modeling heteroscedastic and/or heavy tailed data can give a poor fit, even with a large number of components. This paper explores how well a smooth mixture of symmetric components can capture skewed data. Simulations and applications on real data show that including covariate-dependent skewness in the components can lead to substantially improved performance on skewed data, often using a much smaller number of components. Furthermore, variable selection is effective in removing unnecessary covariates in the skewness, which means that there is little loss in allowing for skewness in the components when the data are actually symmetric. We also introduce smooth mixtures of gamma and log-normal components to model positively-valued response variables.
Keywords: Bayesian inference; Markov chain Monte Carlo; Mixture of Experts; Variable selection
26 pages, August 1, 2010
Full text files
wp245.pdf
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