IDEAS home Printed from
   My bibliography  Save this paper

Bayesian Analysis of the Stochastic Switching Regression Model Using Markov Chain Monte Carlo Methods


  • Maria Odejar

    () (Kansas State University)


In the stochastic switching regression model, it is not known which of several alternative regression models has actually generated the observed dependent variable. This study develops Bayesian methods for estimating the parameters of this model. A difficulty with this approach in this context arises because the direct evaluation of the posterior mean is complicated and cumbersome. An alternative to direct evaluation is the use of Markov Chain Monte Carlo methods. The particular methods examined here are data augmentation and Gibbs sampling, both of which use samples that are drawn from conditional distributions that are easier to derive and more feasible to sample from than the complex joint posterior distribution. The conditional distributions necessary to implement data augmentation and Gibbs sampling are derived in this study. A simulation study compares model estimates obtained using data augmentation, Gibbs sampling, and the maximum-likelihood EM algorithm. Two models are examined, a market-disequilibrium and a structural-change model. In the simulation study, special attention is given to the accuracy and bias of the researcher's prior distributions. The results suggest that data augmentation and Gibbs sampling perform similarly for all cases. When there is little or no bias in the mean of the prior distribution, the Bayesian estimates perform better than maximum-likelihood as long as the standard deviations of the regression coefficients are less than 1.0. When the standard deviations of the regression coefficients are between 1.0 and 2.0, the Bayesian and maximum-likelihood estimates are similar. With moderate bias in the prior mean, the Bayesian and maximum-likelihood estimates perform similarly. Large bias allows the maximum-likelihood method to perform better than the Bayesian estimator as long as the standard deviations of the regression coefficients are small.

Suggested Citation

  • Maria Odejar, 1999. "Bayesian Analysis of the Stochastic Switching Regression Model Using Markov Chain Monte Carlo Methods," Computing in Economics and Finance 1999 822, Society for Computational Economics.
  • Handle: RePEc:sce:scecf9:822

    Download full text from publisher

    File URL:
    File Function: main text
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    1. Beard, T Randolph & Caudill, Steven B & Gropper, Daniel M, 1991. "Finite Mixture Estimation of Multiproduct Cost Functions," The Review of Economics and Statistics, MIT Press, vol. 73(4), pages 654-664, November.
    2. Kon, Stanley J & Jen, Frank C, 1978. "Estimation of Time-Varying Systematic Risk and Performance for Mutual Fund Portfolios: An Application of Switching Regression," Journal of Finance, American Finance Association, vol. 33(2), pages 457-475, May.
    Full references (including those not matched with items on IDEAS)

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    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:sce:scecf9:822. 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: (Christopher F. Baum). General contact details of provider: .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.