Bayesian Analysis of Latent Threshold Dynamic Models
提出一种动态稀疏建模方法，通过潜在阈值机制实现时变参数的自动变量选择，应用于动态回归、向量自回归和波动率模型，提升预测效果和经济解释。
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.