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Bayesian econometrics:conjugate analysis and rejection sampling using mathematica

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  • Ley, Eduardo
  • Steel, Mark F.J.

Abstract

Mathematica is a powerful "system for doing mathematics by computer" which runs on personal computers (Macs and MS-DOS machines), workstations and mainframes. Here we show how Bayesian methods can be implemented in Mathematica. One of the drawbacks of Bayesian techniques is that they are computation-intensive, and every computation is a little different. Since Mathematica is so flexible, it can easily be adapted to solving a number of different Bayesian estimation problems. We illustrate the use of Mathematica functions (i) in a traditional conjugate analysis of the linear regression model and (ii) in a completely nonstandard model -where rejection sampling is used to sample from the posterior.

Suggested Citation

  • Ley, Eduardo & Steel, Mark F.J., 1992. "Bayesian econometrics:conjugate analysis and rejection sampling using mathematica," UC3M Working papers. Economics 2887, Universidad Carlos III de Madrid. Departamento de Economía.
    • Handle: RePEc:cte:werepe:2887
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    Cited by:

    1. Ley, Eduardo & Steel, Mark F J, 1996. "On the Estimation of Demand Systems through Consumption Efficiency," The Review of Economics and Statistics, MIT Press, vol. 78(3), pages 539-543, August.
    2. Jacek Osiewalski & Mark Steel, 1998. "Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models," Journal of Productivity Analysis, Springer, vol. 10(1), pages 103-117, July.

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    Keywords

    Bayes;

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