Macroeconomic Indicator Forecasting with Deep Neural Networks

Thomas Cook and Aaron Smalter Hall ()

No RWP 17-11, Research Working Paper from Federal Reserve Bank of Kansas City

Abstract: Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models {{p}} that exhibit model dependence and have high data demands. {{p}} We explore deep neural networks as an {{p}} opportunity to improve upon forecast accuracy with limited data and while remaining agnostic as to {{p}} functional form. We focus on predicting civilian unemployment using models based on four different neural network architectures. Each of these models outperforms bench- mark models at short time horizons. One model, based on an Encoder Decoder architecture outperforms benchmark models at every forecast horizon (up to four quarters).

Keywords: neural networks; Forecasting; Macroeconomic indicators (search for similar items in EconPapers)
JEL-codes: C14 C45 C53 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2017-09-29
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ets, nep-for, nep-mac and nep-ore
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Citations: View citations in EconPapers (33)

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Working Paper: Macroeconomic Indicator Forecasting with Deep Neural Networks (2019)
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