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Using Simulation Methods for Bayesian Econometric Models

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  • John Geweke

    () (University of Minnesota and FRB)

Abstract

This paper surveys the fundamental principles of subjective Bayesian inference in econometrics and their implementation using posterior simulation methods. The emphasis is on the combination of models and the development of predictive distributions. The paper shows how posterior simulators can facilitate communication between investigators (for example, econometricians) on the one hand and remote clients (for example, decision makers) on the other, enabling clients to vary the prior distributions and functions of interest employed by investigators.

Suggested Citation

  • John Geweke, 1999. "Using Simulation Methods for Bayesian Econometric Models," Computing in Economics and Finance 1999 832, Society for Computational Economics.
  • Handle: RePEc:sce:scecf9:832
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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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