Economic Recession Prediction Using Deep Neural Network

2021/07/21 by Wang, Zihao, Li, Kun, Xia, Steve Q. +1
#FOS: Computer and information sciences #FOS: Economics and business #General Economics (econ.GN) #Machine Learning (cs.LG)

paper · doi:10.48550/arxiv.2107.10980

Abstract

We investigate the effectiveness of different machine learning methodologies in predicting economic cycles. We identify the deep learning methodology of Bi-LSTM with Autoencoder as the most accurate model to forecast the beginning and end of economic recessions in the U.S. 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.

Related

• Can Machine Learning Catch Economic Recessions Using Economic and Market Sentiments?
• Inside the black box: Neural network-based real-time prediction of US recessions
• Predicting Economic Recessions Using Machine Learning Algorithms
• Forecasting Four Business Cycle Phases Using Machine Learning: A Case Study of US and EuroZone
• Nowcasting Recessions using the SVM Machine Learning Algorithm