IDEAS home Printed from https://ideas.repec.org/p/hhs/rbnkwp/0245.html
   My bibliography  Save this paper

Modeling Conditional Densities Using Finite Smooth Mixtures

Author

Listed:
  • Li, Feng

    () (Department of Statistics)

  • Villani, Mattias

    () (Research Department, Central Bank of Sweden)

  • Kohn, Robert

    () (The University of New South Wales)

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.

Suggested Citation

  • Li, Feng & Villani, Mattias & Kohn, Robert, 2010. "Modeling Conditional Densities Using Finite Smooth Mixtures," Working Paper Series 245, Sveriges Riksbank (Central Bank of Sweden).
  • Handle: RePEc:hhs:rbnkwp:0245
    as

    Download full text from publisher

    File URL: http://www.riksbank.com/upload/Dokument_riksbank/Kat_publicerat/WorkingPapers/2010/wp245.pdf
    Download Restriction: no

    More about this item

    Keywords

    Bayesian inference; Markov chain Monte Carlo; Mixture of Experts; Variable selection;

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hhs:rbnkwp:0245. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Lena Löfgren). General contact details of provider: http://edirc.repec.org/data/rbgovse.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.