Abstract
The ability to quickly and accurately classify economic activity into periods of recession and expansion is of great interest to economists and policy makers. Machine Learning methods can potentially be applied to the classification of business cycles. This paper describes two machine learning methods, K-Nearest Neighbor and Neural Networks, and compares them to a Dynamic Factor Markov Switching model for determining business cycle turning points. We conclude that machine learning techniques can offer more accurate classifiers that are worthy of additional study.