Bayesian analysis in the case of an estimated parameter following a stochastic process
AbstractWe 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
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Bibliographic InfoArticle provided by Publishing House "SINERGIA PRESS" in its journal Applied Econometrics.
Volume (Year): 20 (2010)
Issue (Month): 4 ()
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Web page: http://appliedeconometrics.cemi.rssi.ru/
asymptotic covariance matrix; Bayes’ rule; Gaussian process; marginal posterior distribution;
Find related papers by JEL classification:
- C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
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- Aivazian, Sergei, 2008. "Bayesian Methods in Econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 9(1), pages 93-130.
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