http://www.bis.org/cbhub/list/author/author_4803/index.rss Research hub papers by author Feng Li en http://www.riksbank.com/upload/Dokument_riksbank/Kat_publicerat/WorkingPapers/2010/wp245.pdf Sveriges Riksbank Working Papers by Feng Li, Mattias Villani and Robert Kohn Modeling Conditional Densities Using Finite Smooth Mixtures 2010-09-29T17:38:59Z 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. Modeling Conditional Densities Using Finite Smooth Mixtures 2010-09-29T17:38:59Z Abstract http://www.riksbank.com/templates/Page.aspx?id=45006 Full text http://www.riksbank.com/upload/Dokument_riksbank/Kat_publicerat/WorkingPapers/2010/wp245.pdf Robert Kohn Feng Li Mattias Villani Feng Li, Mattias Villani and Robert Kohn 2010-09-29 Sveriges Riksbank Working Papers http://www.riksbank.com/upload/Dokument_riksbank/Kat_publicerat/WorkingPapers/2009/wp233.pdf Sveriges Riksbank Working Papers by Feng Li, Mattias Villani and Robert Kohn Flexible Modeling of Conditional Distributions Using Smooth Mixtures of Asymmetric Student T Densities 2009-11-09T12:39:59Z A general model is proposed for flexibly estimating the density of a continuous response variable conditional on a possibly high-dimensional set of covariates. The model is a finite mixture of asymmetric student-t densities with covariate dependent mixture weights. The four parameters of the components, the mean, degrees of freedom, scale and skewness, are all modelled as functions of the covariates. Inference is Bayesian and the computation is carried out using Markov chain Monte Carlo simulation. To enable model parsimony, a variable selection prior is used in each set of covariates and among the covariates in the mixing weights. The model is used to analyse the distribution of daily stock market returns, and shown to more accurately forecast the distribution of returns than other widely used models for financial data. Flexible Modeling of Conditional Distributions Using Smooth Mixtures of Asymmetric Student T Densities 2009-11-09T12:39:59Z Abstract http://www.riksbank.com/templates/Page.aspx?id=42499 Full text http://www.riksbank.com/upload/Dokument_riksbank/Kat_publicerat/WorkingPapers/2009/wp233.pdf Robert Kohn Feng Li Mattias Villani Feng Li, Mattias Villani and Robert Kohn 2009-11-09 Sveriges Riksbank Working Papers