Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/267295 
Year of Publication: 
2022
Series/Report no.: 
CESifo Working Paper No. 10062
Publisher: 
Center for Economic Studies and ifo Institute (CESifo), Munich
Abstract: 
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.
Subjects: 
uncertainty
skewness
quantile regressions
vector autoregressions
MIDAS
JEL: 
E21
E24
Document Type: 
Working Paper
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