Bayesian Analysis of Latent Threshold Dynamic Models
Bayesian Analysis of Latent Threshold Dynamic Models
复制标题
DOI：
10.1080/07350015.2012.747847
复制
发表时间：
2013-04-01
影响因子：
3
通讯作者：
West, Mike
中科院分区：
文献类型：
--
作者：
Nakajima, Jouchi;West, Mike
本文讨论了多元时间序列分析中动态稀疏建模的一般方法。时变参数被链接到潜在的过程，阈值诱导零值自适应，提供自然的机制，动态变量的包容性/选择。我们讨论贝叶斯模型的规格，分析和预测动态回归，时变向量自回归，和多变量波动率模型使用潜在阈值。一个热门的宏观经济时间序列问题的应用程序说明了一些统计和经济解释方面的方法，以及改进的预测的好处。本文的补充材料可在网上查阅。
We discuss a general approach to dynamic sparsity modeling in multivariate time series analysis. Time-varying parameters are linked to latent processes that are thresholded to induce zero values adaptively, providing natural mechanisms for dynamic variable inclusion/selection. We discuss Bayesian model specification, analysis and prediction in dynamic regressions, time-varying vector autoregressions, and multivariate volatility models using latent thresholding. Application to a topical macroeconomic time series problem illustrates some of the benefits of the approach in terms of statistical and economic interpretations as well as improved predictions. Supplementary materials for this article are available online.