Bayesian estimation and inference for log-ACD models

被引：0
作者：
Richard Gerlach
Shelton Peiris
Edward M. H. Lin
机构：
[1] The University of Sydney,Business School
[2] The University of Sydney,School of Mathematics and Statistics
[3] Academica Sinica,Institute of Statistical Science
来源：
Computational Statistics | 2016年 / 31卷
关键词：
Auto-regressive conditional duration; Weibull; Skewed Student-t; Bayesian; Financial trading;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper adapts Bayesian Markov chain Monte Carlo methods for application to some auto-regressive conditional duration models. Subsequently, the properties of these estimators are examined and assessed across a range of possible conditional error distributions and dynamic specifications, including under error mis-specification. A novel model error distribution, employing a truncated skewed Student-t distribution is proposed and the Bayesian estimator assessed for it. The results of an extensive simulation study reveal that favourable estimation properties are achieved under a range of possible error distributions, but that the generalised gamma distribution assumption is most robust and best preserves these properties, including when it is incorrectly specified. The results indicate that the powerful numerical methods underlying the Bayesian estimator allow more efficiency than the (quasi-) maximum likelihood estimator for the cases considered.
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页码：25 / 48
页数：23
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