Abstract
We investigate the effectiveness of different machine learning methodologies in predicting economic cycles. We identify the deep learning methodology of BiLSTM with autoencoder as the most accurate model to forecast the beginning and end of economic recessions in the United States. We adopt commonly available macro and market-condition features to compare the ability of different machine learning models to generate good predictions both in-sample and out-of-sample. The proposed model is flexible and dynamic when both predictive variables and model coefficients vary over time. It provided good out-of-sample predictions for the past two recessions and early warning about the COVID-19 recession.
