Economic Forecasting With Autoregressive Methods and Neural Networks

54 Pages Posted: 12 Feb 2020

See all articles by James Ming Chen

James Ming Chen

Michigan State University - College of Law

Date Written: January 17, 2020

Abstract

Neural networks can forecast economic data with accuracy matching that of conventional autoregressive methods such as SARIMA and VAR. This study uses dense, recurrent, convolutional, and convnet/RNN hybrids to conduct time-series analysis of interest rates, consumer and producer prices, and labor market data. Training on 14 years of data, neural networks produce accurate 50-year forecasts. Gaps in these forecasts may reveal macroeconomic regime changes. Failures in otherwise accurate neural network forecasts may thus inform theoretical economic hypotheses through unsupervised machine learning.

Suggested Citation

Chen, James Ming, Economic Forecasting With Autoregressive Methods and Neural Networks (January 17, 2020). Available at SSRN: https://ssrn.com/abstract=3521532 or http://dx.doi.org/10.2139/ssrn.3521532

James Ming Chen (Contact Author)

Michigan State University - College of Law ( email )

318 Law College Building
East Lansing, MI 48824-1300
United States

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