Disciplining growth‐at‐risk models with survey of professional forecasters and Bayesian quantile regression
Milan Szabo
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, … Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.