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Bayesian Inference of Trend and Difference-Stationarity

Published online by Cambridge University Press:  11 February 2009

Robert E. McCulloch  and
Ruey S. Tsay
Robert E. McCulloch
Affiliation:
University of Chicago
Ruey S. Tsay
Affiliation:
University of Chicago
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Abstract

This paper proposes a general Bayesian framework for distinguishing between trend- and difference-stationarity. Usually, in model selection, we assume that all of the data were generated by one of the models under consideration. In studying time series, however, we may be concerned that the process is changing over time, so that the preferred model changes over time as well. To handle this possibility, we compute the posterior probabilities of the competing models for each observation. This way we can see if different segments of the series behave differently with respect to the competing models. The proposed method is a generalization of the usual odds ratio for model discrimination in Bayesian inference. In application, we employ the Gibbs sampler to overcome the computational difficulty. The procedure is illustrated by a real example.

Type
Articles
Information
Econometric Theory , Volume 10 , Issue 3-4 , August 1994 , pp. 596 - 608
Copyright
Copyright © Cambridge University Press 1994

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