Parsimony inducing priors for large scale state–space models
促进大规模状态空间模型简约性的先验AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要以动态状态空间模型为方法对象，并明确估计股票日收益的时变条件协方差，涉及时间序列与波动率。
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促进大规模状态空间模型简约性的先验
- 研究问题
- 在含大量潜在状态的贝叶斯状态空间模型中，如何通过先验允许部分状态不随时间变化，从而控制模型复杂度？
- 方法与思路
- 作者对状态空间成分的标准差设置混合四类常用模型的灵活先验，以模拟和例子展示，并用于大规模时变条件协方差估计。
- 材料陈述的贡献
- 摘要称先验可适应不同状态变化情形；标普一百成分股日收益应用包含约五千个时变状态，模型结构允许并行计算。
- 阅读关注点
- 应核对四类先验成分的定义、常数状态如何获得支持、先验敏感性，以及高维协方差约束和并行计算的具体安排。
- 阅读边界
- 仅据所给出版社摘要；简约性是允许部分状态不时变，摘要未提供选择一致性、计算耗时或相对预测改善的保证。
原文摘要
State–space models are commonly used in the engineering, economic, and statistical literature. They are flexible and encompass many well-known statistical models, including random coefficient autoregressive models and dynamic factor models. Bayesian analysis of state–space models has attracted much interest in recent years. However, for large scale models, prior specification becomes a challenging issue in Bayesian inference. In this paper, we propose a flexible prior for state–space models. The proposed prior is a mixture of four commonly entertained models, yet achieving parsimony in high-dimensional systems. Here “parsimony” is represented by the idea that, in a large system, some states may not be time-varying. Our prior for the state–space component’s standard deviation is capable to accommodate different scenarios. Simulation and simple examples are used throughout this paper to demonstrate the performance of the proposed prior. As an application, we consider the time-varying conditional covariance matrices of daily log returns of the components of the S&P 100 index, leading to a state–space model with roughly five thousand time-varying states. Our model for this large system enables us to use parallel computing.