Economic Recession Prediction Using Deep Neural Network

Zihao Wang, Kun Li, Steve Q. Xia and Hongfu Liu

Papers from arXiv.org

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.

Date: 2021-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-for, nep-isf and nep-rmg
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Citations: View citations in EconPapers (2)

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