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

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  • Odejar, Maria Ana E
  • McNulty, Mark S

Abstract

This study develops Bayesian methods for estimating the parameters of a stochastic switching regression model. Markov Chain Monte Carlo methods, data augmentation, and Gibbs sampling are used to facilitate estimation of the posterior means. The main feature of these methods is that the posterior means are estimated by the ergodic averages of samples drawn from conditional distributions, which are relatively simple in form and more feasible to sample from than the complex joint posterior distribution. A simulation study is conducted comparing model estimates obtained using data augmentation, Gibbs sampling, and the maximum likelihood EM algorithm and determining the effects of the accuracy of and bias of the researcher's prior distributions on the parameter estimates. Copyright 2001 by Kluwer Academic Publishers

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  • Odejar, Maria Ana E & McNulty, Mark S, 2001. "Bayesian Analysis of the Stochastic Switching Regression Model Using Markov Chain Monte Carlo Methods," Computational Economics, Springer;Society for Computational Economics, vol. 17(2-3), pages 265-284, June.
    • Handle: RePEc:kap:compec:v:17:y:2001:i:2-3:p:265-84
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    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.
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