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dc.contributor.authorKorobilis, Dimitris
dc.contributor.authorSchröder, Maximilian
dc.description.abstractWe 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 e ects 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 implemennt joint risk scenario analysis.en_US
dc.publisherNorges Banken_US
dc.relation.ispartofseriesWorking paper;9/2023
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.subjectquantile VARen_US
dc.subjectvariational Bayesen_US
dc.subjectdynamic factor modelen_US
dc.subjectJEL: C11en_US
dc.subjectJEL: C32en_US
dc.subjectJEL: E31en_US
dc.subjectJEL: E32en_US
dc.subjectJEL: E37en_US
dc.subjectJEL: E66en_US
dc.titleMonitoring multicountry macroeconomic risken_US
dc.typeWorking paperen_US
dc.subject.nsiVDP::Samfunnsvitenskap: 200::Økonomi: 210::Samfunnsøkonomi: 212en_US

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal