Economic Forecasting With Autoregressive Methods and Neural Networks

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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.