Disciplining growth-at-risk models with survey of professional forecasters and Bayesian quantile regression

Abstract

This study presents a novel and fully probabilistic approach for combining model-based forecasts with surveys or other judgmental forecasts. In our method, survey forecasts are integrated as penalty terms for the model parameters, facilitating a probabilistic exploration of additional insights obtained from surveys. We apply this approach to estimate a growth-at-risk model for real GDP growth in the United States. The results reveal that this additional shrinkage significantly improves prediction performance, with the information from surveys even exerting an influence on the lower tails of the distribution.