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摘要翻译：
利用序贯蒙特卡罗(SMC)的优点，提出了GARCH（广义自回归条件异方差）模型的参数估计和模型选择方法。它提供了一种相对于经典推论的量化估计不确定性的替代方法。即使在较长的时间序列中，模型参数的后验分布也是非正态分布，这突出了贝叶斯方法和有效的后验抽样方法的必要性。对于长时间序列数据，本文还提出了构造SMC分布序列和保留一次交叉验证的有效方法。最后，针对复杂的GARCH型模型--坏环境-好环境模型，给出了一个无偏的似然估计，该估计允许以前文献中没有的精确贝叶斯推断。
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英文标题：
《Efficient Bayesian estimation for GARCH-type models via Sequential Monte
Carlo》
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作者：
Dan Li and Adam Clements and Christopher Drovandi
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最新提交年份：
2020
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分类信息：
一级分类：Statistics 统计学
二级分类：Applications 应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Economics 经济学
二级分类：Econometrics 计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Statistics 统计学
二级分类：Computation 计算
分类描述：Algorithms, Simulation, Visualization
算法、模拟、可视化
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英文摘要：
The advantages of sequential Monte Carlo (SMC) are exploited to develop parameter estimation and model selection methods for GARCH (Generalized AutoRegressive Conditional Heteroskedasticity) style models. It provides an alternative method for quantifying estimation uncertainty relative to classical inference. Even with long time series, it is demonstrated that the posterior distribution of model parameters are non-normal, highlighting the need for a Bayesian approach and an efficient posterior sampling method. Efficient approaches for both constructing the sequence of distributions in SMC, and leave-one-out cross-validation, for long time series data are also proposed. Finally, an unbiased estimator of the likelihood is developed for the Bad Environment-Good Environment model, a complex GARCH-type model, which permits exact Bayesian inference not previously available in the literature.
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PDF链接：
https://arxiv.org/pdf/1906.03828
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