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Posterior Moments Computed By Mixed Integration

Author

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  • van Dijk, H. K.
  • Kloek, T.
  • Boender, C. G. E.

Abstract

A flexible numerical integration method is proposed for the computation of moments of a multivariate posterior density with different tail properties in different directions. The method (called mixed integration) amounts to a combination of classical numerical integration and Monte Carlo integration. Mixed integration is parsimonious in the sense that it makes use of the same parameters as the more restrictive multivariate normal importance function. The method is applied in order to compute the posterior scores of three candidates for a professorship in Operations Research taking into account four different decision criteria.

Suggested Citation

  • van Dijk, H. K. & Kloek, T. & Boender, C. G. E., 1985. "Posterior Moments Computed By Mixed Integration," Econometric Institute Archives 272291, Erasmus University Rotterdam.
    • Handle: RePEc:ags:eureia:272291
    DOI: 10.22004/ag.econ.272291
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    References listed on IDEAS

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    1. Kloek, Tuen & van Dijk, Herman K, 1978. "Bayesian Estimates of Equation System Parameters: An Application of Integration by Monte Carlo," Econometrica, Econometric Society, vol. 46(1), pages 1-19, January.
    2. Lootsma, F. A., 1980. "Saaty's priority theory and the nomination of a senior professor in operations Research," European Journal of Operational Research, Elsevier, vol. 4(6), pages 380-388, June.
    3. van Dijk, H. K. & Kloek, T., 1980. "Further experience in Bayesian analysis using Monte Carlo integration," Journal of Econometrics, Elsevier, vol. 14(3), pages 307-328, December.
    4. van Dijk, H. K. & Kloek, T., 1982. "Posterior Moments Of The Klein-Goldberger Model," Econometric Institute Archives 272269, Erasmus University Rotterdam.
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    Citations

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    Cited by:

    1. BAUWENS, Luc & BOS, Charles S. & VAN DIJK, Herman K., 1999. "Adaptive polar sampling with an application to a Bayes measure of value-at-risk," LIDAM Discussion Papers CORE 1999057, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Bauwens, Luc & Bos, Charles S. & van Dijk, Herman K. & van Oest, Rutger D., 2004. "Adaptive radial-based direction sampling: some flexible and robust Monte Carlo integration methods," Journal of Econometrics, Elsevier, vol. 123(2), pages 201-225, December.
    3. van Dijk, H. K., 1987. "Some Advances In Bayesian Estimation Methods Using Monte Carlo Integration," Econometric Institute Archives 272361, Erasmus University Rotterdam.
    4. van Dijk, H. K. & Hop, J. P. & Louter, A. S., 1986. "An Algorithm For The Computation Of Posterior Moments And Densities Using Simple Importance Sampling," Econometric Institute Archives 272354, Erasmus University Rotterdam.
    5. Luc Bauwens & Charles S. Bos & Herman K. van Dijk, 1998. "Adaptive Polar Sampling: A New MC Technique for the Analysis of Ill-behaved Surfaces," Tinbergen Institute Discussion Papers 98-071/4, Tinbergen Institute.
    6. Hop, J. P. & van Duk, H. K., 1990. "Two Algorithms For The Computation Of Posterior Moments And Densities Using Monte Carlo Integration," Econometric Institute Archives 272483, Erasmus University Rotterdam.
    7. Kooiman, Peter & Van Dijk, Herman K. & Thurik, A. Roy, 1985. "Likelihood diagnostics and Bayesian analysis of a micro-economic disequilibrium model for retail services," Journal of Econometrics, Elsevier, vol. 29(1-2), pages 121-148.
    8. H. K. Van Dijk, 1999. "Some remarks on the simulation revolution in bayesian econometric inference," Econometric Reviews, Taylor & Francis Journals, vol. 18(1), pages 105-112.
    9. Denis Fougère & Thierry Kamionka, 2003. "Bayesian inference for the mover-stayer model in continuous time with an application to labour market transition data," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(6), pages 697-723.
    10. Bauwens, L. & Bos, C.S. & van Dijk, H.K. & van Oest, R.D., 2003. "Explaining Adaptive Radial-Based Direction Sampling," Econometric Institute Research Papers EI 2003-37, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    11. Hoogerheide, L.F. & van Dijk, H.K. & van Oest, R.D., 2007. "Simulation based bayesian econometric inference: principles and some recent computational advances," Econometric Institute Research Papers EI 2007-03, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    12. Vijverberg, Wim P. M., 1997. "Monte Carlo evaluation of multivariate normal probabilities," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 281-307.

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