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 model fit. In this article, we modify the stochastic volatility in mean (SVM) model by introducing state-of-the-art shrinkage techniques that allow for time variation in the degree of shrinkage. Using a real-time inflation forecast exercise, we show that employing more flexible prior distributions on several key parameters sometimes improves forecast performance for the United States, the United Kingdom, and the euro area (EA). Comparing in-sample results reveals that our proposed model yields qualitatively similar insights to the original version of the model.
