Macro-at-Risk in the euro area Expert Group on Macro-at-Risk Time-Series Workstream

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 single-factor 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.