  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.
