Modeling and forecasting macroeconomic downside risk

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Delle Monache, Davide and De Polis, Andrea and Petrella, Ivan (2024) Modeling and forecasting macroeconomic downside risk. Journal of Business and Economic Statistics, 42 (3). pp. 1010-1025. ISSN 0735-0015 (https://doi.org/10.1080/07350015.2023.2277171)

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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.

ORCID iDs

Delle Monache, Davide, De Polis, Andrea ORCID logoORCID: https://orcid.org/0000-0002-0483-2643 and Petrella, Ivan;
  • Item type:Article
    ID code:90203
    Dates:
    Date
    Event
    2024
    Published
    15 December 2023
    Published Online
    1 December 2023
    Accepted
    Subjects:Social Sciences > Economic Theory
    Department:Strathclyde Business School > Economics
    Depositing user: Pure Administrator
    Date deposited:12 Aug 2024 12:34
    Last modified:03 Dec 2024 02:08
    URI:https://strathprints.strath.ac.uk/id/eprint/90203
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