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

Published online by Cambridge University Press:  11 February 2009

Jae-Young Kim
Jae-Young Kim
Affiliation:
University of Florida and State University of New York at Albany
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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.

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Type
Articles
Information
Econometric Theory , Volume 10 , Issue 3-4 , August 1994, pp. 764 - 773
Copyright
Copyright © Cambridge University Press 1994

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References

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