🤖 AI Summary
This paper addresses dynamic variable selection in high-dimensional time-varying regression with pre-specified group structures. We propose a scalable variational Bayesian framework, the first to integrate variational inference into this setting. Our method jointly incorporates dynamic sparsity-inducing priors—encompassing both group-wise sparsity and time-varying shrinkage—high-dimensional time-series modeling, and efficient approximate posterior computation. It achieves a favorable balance between statistical accuracy and computational scalability, making it suitable for large-scale macroeconomic forecasting tasks, such as inflation modeling. In extensive simulations and empirical analyses using real macroeconomic data, the method delivers substantial improvements in both point and density forecasting accuracy. Moreover, it uncovers economically interpretable, time-varying, and group-structured patterns among inflation drivers—revealing how key determinants evolve and cluster over time.
📝 Abstract
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 based on 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.
