We propose a multicountry quantile factor augmeneted vector autoregression (QFAVAR) to model heterogeneities both across countries and across characteristics of the distributions of macroeconomic time series. The presence of quantile factors allows for summarizing these two heterogeneities in a parsimonious way. We develop two algorithms for posterior inference that feature varying level of trade-off between estimation precision and computational speed. Using monthly data for the euro area, we establish the good empirical properties of the QFAVAR as a tool for assessing the effects of global shocks on country-level macroeconomic risks. In particular, QFAVAR short-run tail forecasts are more accurate compared to a FAVAR with symmetric Gaussian errors, as well as univariate quantile autoregressions that ignore comovements among quantiles of macroeconomic variables. We also illustrate how quantile impulse response functions and quantile connectedness measures, resulting from the new model, can be used to implement joint risk scenario analysis.

翻译：我们提出了一种多国分位数因子增强向量自回归（QFAVAR）模型，用于建模跨国异质性与宏观经济时间序列分布特征中的异质性。分位数因子的引入使得能够以简洁的方式概括这两类异质性。我们开发了两种后验推断算法，它们在估计精度与计算速度之间实现了不同程度的权衡。基于欧元区的月度数据，我们验证了QFAVAR作为评估全球冲击对国别宏观经济风险影响工具的良好实证性质。特别地，与具有对称高斯误差的FAVAR以及忽略宏观经济变量分位数间协同变动的单变量分位数自回归模型相比，QFAVAR的短期尾部预测更为精准。我们还展示了如何利用新模型生成的分位数脉冲响应函数与分位数关联度指标，实施联合风险情景分析。