A Predictive Framework for Multi-Horizon Financial Crises Forecasting using Macro-Economic Data

US economy is driven by complex dynamics and interplay, often stemming from consumer spending, supply- demand associations, private market interactions, and government interventions. Intricacies within such a mixed economic ecosystem make it difficult to f orecast e vents l ike economic downturns. Recent financial c rises h ave s purred a d ebate over the efficacy o f e arly w arning s ystems, b ased o n traditional econometric modeling approaches, to forewarn against such events. Despite several advancements, accurate forecasting of these kinds of crises events remains elusive due to inherently complex interactions among the different economic constituents.In this paper, we propose a novel framework to predict economic crises events. We accomplish this task by deploying a multi-stage methodology that includes evaluation of macroeconomic indicators, a predictive modeling framework comprising of a diverse set of traditional and advanced forecasting models, and an evaluation approach to capture the contributions of different macroeconomic factors on multi-horizon forecasts, with good confidence. I n t his r esearch, w e l everage t he framework to forecast US economic downturns. Our results on the real test data outperform those from the survey of professional forecasters and confirm t he e fficacy of th e pr oposed ap proach to predict economic downturns ahead of multiple leading periods. The proposed solution can aid economists and policymakers prevent or proactively prepare against such events through appropriate monetary policies thereby minimizing the risk to the economy.