Forecasting Macroeconomic Risk in Real Time: Great and Covid-19 Recessions

Abstract

We show that financial variables contribute to the forecast of GDP growth during the Great Recession, providing additional insights on both first and higher moments of the GDP growth distribution. If a recession is due to an unforeseen shock (such as the Covid-19 recession), financial variables serve policymakers in providing timely warnings about the severity of the crisis and the macroeconomic risk involved, because downside risks increase as financial stress and corporate spreads become tighter. We use quantile regression and the skewed t-distribution and evaluate the forecasting properties of models using out-of-sample metrics with real-time vintages.