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Bayesian analysis in the case of an estimated parameter following a stochastic process

Author

Listed:
  • Slutskin, Lev

    (Institute of Economics of the Russian Academy of Sciences (IERAS))

Abstract

We perform Bayesian analysis of the sequence of unknown means mi given observations Xi under the assumption that, for any k > 0, the first k members X1, X2, …, Xk are normally distributed with the mean (m1,…, mk ) and a known covariance matrix. It is assumed that the parameters m1,…, mk,… follow a Gaussian process We prove that, for any fixed k, the covariance matrices of marginal posterior distributions converge In the case of a Gaussian AR(1) process analytic expression for the asymptotic posterior structure is given

Suggested Citation

  • Slutskin, Lev, 2010. "Bayesian analysis in the case of an estimated parameter following a stochastic process," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 20(4), pages 119-131.
    • Handle: RePEc:ris:apltrx:0069
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    References listed on IDEAS

    as
    1. Aivazian, Sergei, 2008. "Bayesian Methods in Econometrics," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 9(1), pages 93-130.
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    Cited by:

    1. Demeshev, Boris & Malakhovskaya, Oxana, 2016. "BVAR mapping," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 43, pages 118-141.

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    More about this item

    Keywords

    asymptotic covariance matrix; Bayes’ rule; Gaussian process; marginal posterior distribution;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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