Dynamic shrinkage in time‐varying parameter stochastic volatility in mean models

Successful forecasting models strike a balance between parsimony and flexibility. This is often achieved by employing suitable shrinkage priors that penalize model complexity but also reward fit. In this article, we modify the stochastic volatility in mean (SVM) introducing state-of-the-art techniques allow for time variation degree of shrinkage. Using real-time inflation forecast exercise, show more flexible prior distributions on several key parameters sometimes improves performance United States, Kingdom, euro area (EA). Comparing in-sample results reveals our proposed yields qualitatively similar insights to original version model.