Working Paper

Macroeconomic Indicator Forecasting with Deep Neural Networks

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;

JEL Classification: C14; C45; C53;

Bibliographic Information

Provider: Federal Reserve Bank of Kansas City

Part of Series: Research Working Paper

Publication Date: 2017-09-04

Number: RWP 17-11

Pages: 38 pages