Data and replication information for "Uncertainty, skewness, and the business cycle through the MIDAS lens" by Efrem Castelnuovo and Lorenzo Mori; published in Journal of Applied Econometrics, 2024. 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 financial conditions improves the predictive power of an otherwise quarterly-only model. We combine selected quantiles of the estimated conditional distribution to produce novel 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. Business cycle effects are significantly downplayed if we consider a quarterly-only quantile regression model. We find the endogenous response of skewness to substantially amplify the recessionary effects of uncertainty shocks. Finally, we construct a monthly-frequency version of our uncertainty measure and document the robustness of our findings.
