Nowcasting Recessions using the SVM Machine Learning Algorithm
This addresses the challenge for economists and policymakers of timely recession detection, which is typically delayed by months, though it is an incremental application of an existing method to a new domain.
The paper tackled the problem of nowcasting recessions by applying Support Vector Machines (SVM) to predict the beginning and end of recessions in real time, achieving excellent predictive performance.
We introduce a novel application of Support Vector Machines (SVM), an important Machine Learning algorithm, to determine the beginning and end of recessions in real time. Nowcasting, "forecasting" a condition about the present time because the full information about it is not available until later, is key for recessions, which are only determined months after the fact. We show that SVM has excellent predictive performance for this task, and we provide implementation details to facilitate its use in similar problems in economics and finance.