In this paper, we provide a Doob-style consistency theorem for
stationary models. Many applications involving Bayesian inference deal
with non independent and identically distributed data, in particular, with
stationary data. However, for such models, there is still a theoretical
gap to be filled regarding the asymptotic properties of Bayesian
procedures. The primary goal to be achieved is establishing consistency of
the sequence of posterior distributions. Here we provide an answer to the
problem. Bayesian methods have recently gained growing popularity in
economic modeling, thus implying the timeliness of the present paper.
Indeed, we secure Bayesian procedures against possible inconsistencies. No
results of such a generality are known up to now.
Email your librarian or administrator to recommend adding this journal to your organisation's collection.