Modeling and forecasting macroeconomic downside risk

Davide Delle Monache, Andrea De Polis, Ivan Petrella*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)
1 Downloads (Pure)

Abstract

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.
Original languageEnglish
Pages (from-to)1010-1025
Number of pages16
JournalJournal of Business and Economic Statistics
Volume42
Issue number3
Early online date15 Dec 2023
DOIs
Publication statusPublished - 2024

Keywords

  • business cycle
  • downside risk
  • skewness
  • balance of risks

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