A Bayesian Analysis of Unit Roots and Structural Breaks in the Level, Trend, and Error Variance of Autoregressive Models of Economic Series
In this article, a Bayesian approach is suggested to compare unit root models with stationary autoregressive models when the level, the trend, and the error variance are subject to structural changes (known as breaks) of an unknown date. Ignoring structural breaks in the error variance may be responsible for not rejecting the unit root hypothesis, even if allowance is made in the inferential procedures for breaks in the mean. The article utilizes analytic and Monte Carlo integration techniques for calculating the marginal likelihoods of the models under consideration, in order to compute the posterior model probabilities. The performance of the method is assessed by simulation experiments. Some empirical applications of the method are conducted with the aim to investigate if it can detect structural breaks in financial series, especially with changes in the error variance.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 30 (2011)
Issue (Month): 2 ()
|Contact details of provider:|| Web page: http://www.tandfonline.com/LECR20|
|Order Information:||Web: http://www.tandfonline.com/pricing/journal/LECR20|
When requesting a correction, please mention this item's handle: RePEc:taf:emetrv:v:30:y:2011:i:2:p:208-249. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ()
If references are entirely missing, you can add them using this form.