Bayesian consistency for stationary models

Lijoi, Antonio and Prunster, Igor and Walker, Stephen G. (2007) Bayesian consistency for stationary models. Econometric Theory, 23 (4). pp. 749-759. ISSN 0266-4666. (The full text of this publication is not available from this repository)

The full text of this publication is not available from this repository. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1017/S0266466607070314

Abstract

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.

Item Type: Article
Subjects: Q Science > QA Mathematics (inc Computing science)
Divisions: Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Stephen Holland
Date Deposited: 19 Dec 2007 18:56
Last Modified: 25 Jun 2014 10:43
Resource URI: http://kar.kent.ac.uk/id/eprint/1436 (The current URI for this page, for reference purposes)
  • Depositors only (login required):