Bayesian Asymptotic Theory in a Time Series Model with a Possible Nonstationary Process
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.
Volume (Year): 10 (1994)
Issue (Month): 3-4 (August)
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