Bayesian Analysis of Latent Threshold Dynamic Models

被引：113

作者：

Nakajima, Jouchi ^{[1
]}

West, Mike ^{[1
]}

机构：

[1] Duke Univ, Dept Stat Sci, Durham, NC 27708 USA

来源：

JOURNAL OF BUSINESS & ECONOMIC STATISTICS | 2013年 / 31卷 / 02期

基金：

美国国家科学基金会;

关键词：

Dynamic graphical models; Macroeconomic time series; Multivariate volatility; Sparse time-varying VAR models; Time-varying variable selection; STOCHASTIC VOLATILITY; SELECTION;

D O I：

10.1080/07350015.2012.747847

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

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.

引用

页码：151 / 164

页数：14