3/2026 Macro-at-Risk in the euro area
Occacional Papers of Eesti Pank 3/2026
Expert Group on Macro-at-Risk
Time-Series Workstream
Mohammed Chahad (editor)
Matteo Mogliani (editor)
Chryso Aristidou
Marta Bańbura
Dmitry Kulikov
Carlos Montes-Galdón
Bettina Landau
Baptiste Meunier
Florens Odendahl
Claudia Pacella
Joan Paredes
Antoine Sigwalt
Paulo Rodrigues
Markus Roth
Anastasia Theofilakou
This paper introduces reduced-form macroeconometric tools, emphasising quantile regression models, to identify key risk drivers for the euro area economy and assess risks around the baseline ECB/Eurosystem staff macroeconomic projections for the euro area inflation and growth. The analysis uses a large number of risk factors, going beyond the usual financial factors, employing a sequential selection approach with robustness checks. To support the analysis a MATLAB toolbox (M@RX) was developed, incorporating several quantile regression-based model classes with a novel parametric tilting methodology and a copula approach for transforming predictive densities across frequencies. This paper contributes to the literature on the treatment of the COVID-era data in quantile regression models. Results indicate that the predictive content of risk factors is horizon, time and objective-dependent. For example, labour market indicators are particularly relevant for assessing upside inflation risks, but to a time-varying extent and with limited predictive power for downside risks. Conversely, uncertainty, money and credit indicators perform better for downside inflation risks. As regards risks to growth, the results confirm the established role of financial conditions, while also highlighting the relevance of monetary indicators, particularly for downside risks. Combined risk factor frameworks – with several different risk indicators – tend to systematically outperform singlefactor specifications for density forecasting, due to complementarities across risk indicator groups. An empirical application highlights the policy relevance of these tools, as they provide timely signals and accurately track the direction of realised outcomes. Given the time-varying and state-dependent nature of their predictive performance, a regular performance assessment of the specifications is recommended to maintain reliability.
Keywords: Macro-at-Risk, tail risks, quantile regression, forecasting, density forecasts.
JEL codes: C22, C53, E27, E37
DOI: 10.23656//24613800/032026/0237
This occasional paper is a report by the Time-Series Workstream of the joint WGEM and WGF Expert
Group on Macro-at-Risk. The report is also published in the Occasional Paper series of the European Central Bank (No 396).
This paper should not be taken as representing the views of Eesti Pank or the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of either Eesti Pank or the ECB.
