Efficient Posterior Simulation for Cointegrated Models with Priors on the Cointegration Space

Gary Koop, Roberto Leon-Gonzalez and Rodney Strachan

Econometric Reviews, 2010, vol. 29, issue 2, 224-242

Abstract: A message coming out of the recent Bayesian literature on cointegration is that it is important to elicit a prior on the space spanned by the cointegrating vectors (as opposed to a particular identified choice for these vectors). In previous work, such priors have been found to greatly complicate computation. In this article, we develop algorithms to carry out efficient posterior simulation in cointegration models. In particular, we develop a collapsed Gibbs sampling algorithm which can be used with just-identifed models and demonstrate that it has very large computational advantages relative to existing approaches. For over-identifed models, we develop a parameter-augmented Gibbs sampling algorithm and demonstrate that it also has attractive computational properties.

Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (30)

Downloads: (external link)
http://hdl.handle.net/10.1080/07474930903382208 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: Efficient Posterior Simulation for Cointegrated Models with Priors On the Cointegration Space (2006)
Software Item: RATS program to demonstrate Gibbs sampling in a cointegrated model
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:emetrv:v:29:y:2010:i:2:p:224-242

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/LECR20

DOI: 10.1080/07474930903382208

Access Statistics for this article

Econometric Reviews is currently edited by Dr. Essie Maasoumi

More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().