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.