Uncertainty, Skewness, and the Business Cycle through the MIDAS Lens

Arbeitspapier

We employ a mixed-frequency quantile regression approach to model the time-varying conditional distribution of the US real GDP growth rate. We show that monthly information on the US financial cycle improves the predictive power of an otherwise quarterly-only model. We combine selected quantiles of the estimated conditional distribution to produce measures of uncertainty and skewness. Embedding these measures in a VAR framework, we show that unexpected changes in uncertainty are associated with an increase in (left) skewness and a downturn in real activity. Empirical findings related to VAR impulse responses and forecast error variance decomposition are shown to depend on the inclusion/omission of monthly-level information on financial conditions when estimating real GDP growth's conditional density. Effects are significantly downplayed if we consider a quarterly-only quantile regression model. A counterfactual simulation conducted by shutting down the endogenous response of skewness to uncertainty shocks shows that skewness substantially amplifies the recessionary effects of uncertainty.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: CESifo Working Paper ; No. 10062

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Macroeconomics: Consumption; Saving; Wealth ; Employment; Unemployment; Wages; Intergenerational Income Distribution; Aggregate Human Capital; Aggregate Labor Productivity
• Thema: uncertainty ; skewness ; quantile regressions ; vector autoregressions ; MIDAS

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Castelnuovo, Efrem ; Mori, Lorenzo
• Ereignis: Veröffentlichung
• (wer): Center for Economic Studies and ifo Institute (CESifo)
• (wo): Munich
• (wann): 2022

Weitere Informationen

• Handle: http://hdl.handle.net/10419/267295
• Letzte Aktualisierung: 10.03.2025, 11:42 MEZ