Scalable Variational Bayes Inference for Dynamic Variable Selection
提出一种变分贝叶斯方法，用于高维回归模型中具有分组结构的时变参数和预测变量的动态变量选择，在模拟和通胀预测中比现有方法更准确高效。
We develop a variational Bayes approach for dynamic variable selection in high-dimensional regression models with time-varying parameters and predictors that exhibit a predefined group structure. Through comprehensive simulation studies, we demonstrate that our method yields more accurate parameter estimates than existing Bayesian static and dynamic variable selection approaches while maintaining computational efficiency. We illustrate the performance of our approach within the context of a popular problem in economics: forecasting inflation using a large set of macroeconomic predictors. Our approach demonstrates significant improvements in out-of-sample point- and density-forecasting accuracy. A retrospective analysis of the time-varying parameter estimates reveals economically interpretable patterns in inflation dynamics.