Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

Bayesian Inference of Trend- and Difference-Stationarity

Robert E. McCulloch and Ruey S. Tsay
Econometric Theory
Vol. 10, No. 3/4, Symposium Double Issue: Bayes Methods and Unit Roots (Aug. - Oct., 1994), pp. 596-608
Stable URL: http://www.jstor.org/stable/3532551
Page Count: 13
  • Read Online (Free)
  • Download ($49.00)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Bayesian Inference of Trend- and Difference-Stationarity
Preview not available

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.

Page Thumbnails

  • Thumbnail: Page 
596
    596
  • Thumbnail: Page 
597
    597
  • Thumbnail: Page 
598
    598
  • Thumbnail: Page 
599
    599
  • Thumbnail: Page 
600
    600
  • Thumbnail: Page 
601
    601
  • Thumbnail: Page 
602
    602
  • Thumbnail: Page 
603
    603
  • Thumbnail: Page 
604
    604
  • Thumbnail: Page 
605
    605
  • Thumbnail: Page 
606
    606
  • Thumbnail: Page 
607
    607
  • Thumbnail: Page 
608
    608