RWP 17-11, September 2017
Economic policymaking relies upon accurate forecasts of economic conditions. Current methods for unconditional forecasting are dominated by inherently linear models that exhibit model dependence and have high data demands. We explore deep neural networks as an opportunity to improve upon forecast accuracy with limited data while remaining agnostic as to functional form. Specifically, we predict civilian unemployment using models based on four different neural network architectures. Each of these models outperforms benchmark models at short time horizons. One model, based on an encoder decoder architecture, outperforms benchmark models at every forecast horizon (up to four quarters).
zipAdditional Files: This archive contains reproduction code for each of the models discussed in this paper. The code is suitable for inspection, but will require modification and additional data to run.
JEL Classification: C45, C53, C14
Cook, Thomas R., and Aaron Smalter Hall. “Macroeconomic Indicator Forecasting with Deep Neural Networks.” Federal Reserve Bank of Kansas City, Research Working Paper 17-11, September. Available at External Linkhttps://doi.org/10.18651/RWP2017-11