Bayesian Asymptotic Theory in a Time Series Model with a Possible Nonstationary Process
Bayesian Asymptotic Theory in a Time Series Model with a Possible Nonstationary Process
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DOI：
10.1017/s0266466600008768
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发表时间：
1994-08
影响因子：
0.8
通讯作者：
Jae-Young Kim
中科院分区：
文献类型：
--
作者：
Jae-Young Kim
贝叶斯后验的渐近正态性是平稳动态模型或非动态模型的一个众所周知的结果。本文将分析扩展到一个可能的非平稳过程的时间序列模型。我们拼出的条件下，即使真正的数据生成过程是一个非平稳过程的后验渐近正态性。
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.