Australia Department of Econometrics and Business Statistics Bayesian Analysis of the Stochastic Conditional Duration Model Bayesian Analysis of the Stochastic Conditional Duration Model
Australia Department of Econometrics and Business Statistics Bayesian Analysis of the Stochastic Conditional Duration Model Bayesian Analysis of the Stochastic Conditional Duration Model
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澳大利亚计量经济学和商业统计系 随机条件持续时间模型的贝叶斯分析 随机条件持续时间模型的贝叶斯分析
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通讯作者：
G. Martin
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作者：
C. Strickland;Catherine S. Forbes;G. Martin
提出了一种估计随机条件持续时间模型的贝叶斯马尔可夫链蒙特卡罗方法。交易间持续时间的条件均值被建模为一个潜在的随机过程，持续时间的条件分布具有正支持。所采用的采样方案是Gibbs和Metropolis Hastings算法的混合，其中潜在向量以块为单位采样。结果表明，该方法优于拟极大似然方法，且混合速度快于单步算法。该方法以澳大利亚盘中股票市场数据为例加以说明。作者还想感谢Ralph Snyder以及莫纳什大学研讨会和2003年澳大利亚计量经济学会会议的参与者，他们对论文早期草稿的一些非常有帮助的评论。
A Bayesian Markov Chain Monte Carlo methodology is developed for estimating the stochastic conditional duration model. The conditional mean of durations between trades is modelled as a latent stochastic process, with the conditional distribution of durations having positive support. The sampling scheme employed is a hybrid of the Gibbs and Metropolis Hastings algorithms, with the latent vector sampled in blocks. The suggested approach is shown to be preferable to the quasi-maximum likelihood approach, and its mixing speed faster than that of an alternative single-move algorithm. The methodology is illustrated with an application to Australian intraday stock market data. The authors would also like to thank Ralph Snyder and participants in seminars at Monash University and the 2003 Australasian Meetings of the Econometric Society, for some very helpful comments on an earlier draft of the paper.