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

Lev Slutskin

Applied Econometrics, 2010, vol. 20, issue 4, 119-131

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

Keywords: asymptotic covariance matrix; Bayes’ rule; Gaussian process; marginal posterior distribution (search for similar items in EconPapers)
JEL-codes: C11 (search for similar items in EconPapers)
Date: 2010
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Citations: View citations in EconPapers (1)

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