Modeling and Forecasting Macroeconomic Downside Risk

We model permanent and transitory changes of the predictive density of U.S. GDP growth. A substantial increase in downside risk to U.S. economic growth emerges over the last 30 years, associated with the long-run growth slowdown started in the early 2000s. Conditional skewness moves procyclically, implying negatively skewed predictive densities ahead and during recessions, often anticipated by deteriorating financial conditions. Conversely, positively skewed distributions characterize expansions. The modeling framework ensures robustness to tail events, allows for both dense or sparse predictor designs, and delivers competitive out-of-sample (point, density and tail) forecasts, improving upon standard benchmarks.

Automated summary

This study models the permanent and transitory changes of the predictive density of U.S. GDP growth. It finds a significant increase in downside risk to U.S. economic growth over the last 30 years, particularly during the long-run growth slowdown since the early 2000s. Conditional skewness exhibits a cyclical pattern, with negatively skewed predictive densities preceding and during recessions, often associated with deteriorating financial conditions. In contrast, expansions are characterized by positively skewed distributions. The modeling framework ensures robustness to extreme events, allows for flexible predictor designs, and provides competitive out-of-sample forecasts (point, density, and tail) compared to standard benchmarks.