Bayesian Asymptotic Theory in a Time Series Model with a Possible Nonstationary Process

Kim, Jae-Young

Econometric Theory, 1994, vol. 10, issue 3-4, pages 764-773

Abstract: Asymptotic normality of the Bayesian posterior is a well-known result for stationary dynamic models or nondynamic models. This paper extends the analysis to a time series model with a possible nonstationary process. We spell out conditions under which asymptotic normality of the posterior is obtained even if the true data-generation process is a nonstationary process.

Date: 1994
View citations in EconPapers

Downloads: (external link)
http://journals.cambridge.org/abstract_S0266466600008768 link to article abstract page (text/html)

Related works:
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: http://EconPapers.repec.org/RePEc:cup:etheor:v:10:y:1994:i:3-4:p:764-773_00

Access Statistics for this article

More articles in Econometric Theory from Cambridge University Press
Address: The Edinburgh Building, Shaftesbury Road, Cambridge CB2 2RU UK
Series data maintained by Mike Eden ().