Economic Recession Prediction Using Deep Neural Network
General Economics
2021-07-26 v1 Machine Learning
Economics
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.
Cite
@article{arxiv.2107.10980,
title = {Economic Recession Prediction Using Deep Neural Network},
author = {Zihao Wang and Kun Li and Steve Q. Xia and Hongfu Liu},
journal= {arXiv preprint arXiv:2107.10980},
year = {2021}
}