Scalable Variational Bayes Inference for Dynamic Variable Selection

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 esti- mates than existing Bayesian static and dynamic variable selection approaches while maintaining computa- tional 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.