Bayesian Inference in Cointegrated I (2) Systems: A Generalization of the Triangular Model
AbstractThis paper generalizes the cointegrating model of Phillips (1991) to allow for I (0), I (1) and I (2) processes. The model has a simple form that permits a wider range of I (2) processes than are usually considered, including a more flexible form of polynomial cointegration. Further, the specification relaxes restrictions identified by Phillips (1991) on the I (1) and I (2) cointegrating vectors and restrictions on how the stochastic trends enter the system. To date there has been little work on Bayesian I (2) analysis and so this paper attempts to address this gap in the literature. A method of Bayesian inference in potentially I (2) processes is presented with application to Australian money demand using a Jeffreys prior and a shrinkage prior.
Download InfoIf 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.
Bibliographic InfoArticle provided by Taylor & Francis Journals in its journal Econometric Reviews.
Volume (Year): 26 (2007)
Issue (Month): 2-4 ()
Contact details of provider:
Web page: http://www.tandfonline.com/LECR20
Other versions of this item:
- Rodney W. Strachan, 2005. "Bayesian Inference in Cointegrated I (2) Systems: a Generalisation of the Triangular Model," Discussion Papers in Economics 05/14, Department of Economics, University of Leicester.
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Justyna Wróblewska, 2009. "Bayesian Model Selection in the Analysis of Cointegration," Central European Journal of Economic Modelling and Econometrics, CEJEME, vol. 1(1), pages 57-69, March.
- Tsay, Ruey S. & Ando, Tomohiro, 2012. "Bayesian panel data analysis for exploring the impact of subprime financial crisis on the US stock market," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3345-3365.
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.