IDEAS home Printed from https://ideas.repec.org/p/ags/eureia/272354.html
   My bibliography  Save this paper

An Algorithm For The Computation Of Posterior Moments And Densities Using Simple Importance Sampling

Author

Listed:
  • van Dijk, H. K.
  • Hop, J. P.
  • Louter, A. S.

Abstract

In earlier work (van Dijk (1984, Chapter 3)) one of the authors discussed the use of Monte Carlo integration methods for the computation of the multivariate integrals that are defined in the posterior moments and the liosterior densities of the parameters of interest of econometric models. In the present paper we describe the computational steps of one Monte Carlo method, mentioned In that work, which is known in the literature as importance sampling. Further, we have prepared a set of standard programs, which may be used for the implementation of a simple case of importance sampling. The computer programs have been written in Fortran.

Suggested Citation

  • 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.
    • Handle: RePEc:ags:eureia:272354
    DOI: 10.22004/ag.econ.272354
    as

    Download full text from publisher

    File URL: https://ageconsearch.umn.edu/record/272354/files/erasmus188.pdf
    Download Restriction: no
    File URL: https://ageconsearch.umn.edu/record/272354/files/erasmus188.pdf?subformat=pdfa
    Download Restriction: no
    File URL: https://libkey.io/10.22004/ag.econ.272354?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Geweke, John, 1986. "Exact Inference in the Inequality Constrained Normal Linear Regression Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(2), pages 127-141, April.
    2. Van Dijk, Herman K. & Kloek, Teun & Boender, C. Guus E., 1985. "Posterior moments computed by mixed integration," Journal of Econometrics, Elsevier, vol. 29(1-2), pages 3-18.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Samer A. Kharroubi, 2018. "Posterior simulation via the exponentially tilted signed root log-likelihood ratio," Computational Statistics, Springer, vol. 33(1), pages 213-234, March.
    2. Baştürk, Nalan & Grassi, Stefano & Hoogerheide, Lennart & Opschoor, Anne & van Dijk, Herman K., 2017. "The R Package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i01).
    3. van Dijk, H. K., 1987. "Some Advances In Bayesian Estimation Methods Using Monte Carlo Integration," Econometric Institute Archives 272361, Erasmus University Rotterdam.
    4. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. VAN DIJK, Herman K., 1987. "Some advances in Bayesian estimations methods using Monte Carlo Integration," LIDAM Reprints CORE 783, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. 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.
    3. James A. Chalfant & Richard S. Gray & Kenneth J. White, 1991. "Evaluating Prior Beliefs in a Demand System: The Case of Meat Demand in Canada," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 73(2), pages 476-490.
    4. 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.
    5. Heckelei, Thomas & Mittelhammer, Ronald C., 1996. "Bayesian Bootstrap Analysis of Systems of Equations," Discussion Papers 18786, University of Bonn, Institute for Food and Resource Economics.
    6. 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.
    7. Heckelei, Thomas & Mittelhammer, Ron C., 2003. "Bayesian bootstrap multivariate regression," Journal of Econometrics, Elsevier, vol. 112(2), pages 241-264, February.
    8. HOOGERHEIDE, Lennart F. & VAN DIJK, Herman K. & VAN OEST, Rutger D., 2007. "Simulation based Bayesian econometric inference: principles and some recent computational advances," LIDAM Discussion Papers CORE 2007015, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    9. Dorfman, Jeffrey H., 1995. "A numerical bayesian test for cointegration of AR processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 289-324.
    10. Luc Bauwens & Charles S. Bos & Herman K. van Dijk, 1999. "Adaptive Polar Sampling with an Application to a Bayes Measure of Value-at-Risk," Tinbergen Institute Discussion Papers 99-082/4, Tinbergen Institute.
    11. Fernandez-Cornejo, Jorge, 1992. "Short- And Long-Run Demand And Substitution Of Agricultural Inputs," Northeastern Journal of Agricultural and Resource Economics, Northeastern Agricultural and Resource Economics Association, vol. 21(1), pages 1-14, April.
    12. 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.
    13. Caroline Khan & Mike G. Tsionas, 2021. "Constraints in models of production and cost via slack-based measures," Empirical Economics, Springer, vol. 61(6), pages 3347-3374, December.
    14. Koop, Gary & Poirier, Dale J., 2004. "Bayesian variants of some classical semiparametric regression techniques," Journal of Econometrics, Elsevier, vol. 123(2), pages 259-282, December.
    15. Ardia, David & Hoogerheide, Lennart F., 2010. "Efficient Bayesian estimation and combination of GARCH-type models," MPRA Paper 22919, University Library of Munich, Germany.
    16. Tai-Hsin Huang & Yi-Huang Chiu & Chih-Ying Mao, 2021. "Imposing Regularity Conditions to Measure Banks’ Productivity Changes in Taiwan Using a Stochastic Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 28(2), pages 273-303, June.
    17. Sadorsky, P. A., 1989. "Measuring Resource Scarcity in Non-renewable Resources with Inequality Constrained Estimation," Queen's Institute for Economic Research Discussion Papers 275216, Queen's University - Department of Economics.
    18. 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.
    19. David Lander & David Gunawan & William Griffiths & Duangkamon Chotikapanich, 2020. "Bayesian assessment of Lorenz and stochastic dominance," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 53(2), pages 767-799, May.
    20. Steel, Mark F. J., 1991. "A Bayesian analysis of simultaneous equation models by combining recursive analytical and numerical approaches," Journal of Econometrics, Elsevier, vol. 48(1-2), pages 83-117.

    Corrections

    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:ags:eureia:272354. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: AgEcon Search (email available below). General contact details of provider: https://edirc.repec.org/data/feeurnl.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.