This paper introduces macroeconometric tools to identify key risk drivers for the euro area economy and assess risks around baseline projections for inflation and growth. The analysis uses a large number of risk factors, including non-financial factors, and employs a sequential selection approach with robustness checks. A MATLAB toolbox was developed to support the analysis, incorporating quantile regression models and a novel parametric tilting methodology. The paper contributes to the literature on treating COVID-era data in quantile regression models, providing new insights into risk factor analysis. The results show that the predictive content of risk factors depends on the horizon, time, and objective, highlighting the complexity of risk assessment. Labour market indicators are found to be relevant for assessing upside inflation risks, but their predictive power is limited for downside risks. In contrast, uncertainty, money, and credit indicators perform better for downside inflation risks, while financial conditions and monetary indicators are relevant for growth risks. The study finds that combined risk factor frameworks outperform single-factor specifications for density forecasting due to complementarities across risk indicator groups. The empirical application demonstrates the policy relevance of these tools, providing timely signals and accurately tracking realised outcomes. Regular performance assessment of the specifications is recommended to maintain reliability, given the time-varying and state-dependent nature of their predictive performance.