Uncertainty, Skewness, and the Business Cycle Through the MIDAS Lens
采用混合频率分位数回归方法，利用月度金融状况信息预测美国实际GDP增长率的条件分布，构建不确定性和偏度指标，发现不确定性冲击会加剧经济衰退，且偏度的内生反应放大了这种效应。
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 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.