We guide the reader through key statistical techniques for monitoring and forecasting macroeconomic risk. Moving beyond standard linear point forecasts, we demonstrate how to construct flexible conditional distributions of future GDP growth. We show that several methods can be leveraged to achieve this goal: quantile regression, Markov switching models, and large-scale techniques. Since it captures the likelihood of all possible future outcomes, the conditional distribution of future GDP growth serves as an ideal tool for assessing macroeconomic vulnerabilities. The insights presented in this paper have implications for policymakers, practitioners, and academics.