  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 note, we modify
the stochastic volatility in mean (SVM) model proposed in Chan (2017) 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
slightly improves forecast performance for the United States (US), the United
Kingdom (UK) 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.
