Full Citation

Title: ‘Seeing’ the Future: Improving Macroeconomic Forecasts with Spatial Data Using Recurrent Convolutional Neural Networks

Citation Type: Working Paper

Publication Year: 2023

ISBN:

ISSN:

DOI: 10.2139/SSRN.4350048

NSFID:

PMCID:

PMID:

Abstract: I evaluate whether incorporating sub-national trends improves macroeconomic fore-casting accuracy in a deep machine learning framework. Specifically, I adopt a computer vision setting by transforming U.S. economic data into a ‘video’ series of geographic ‘images’ and utilizing a recurrent convolutional neural network to extract spatiotemporal features. This spatial forecasting model outperforms equivalent methods based on country-level data and achieves a 0.14 percentage point average error when forecasting out-of-sample monthly percentage changes in real GDP over a twelve-month horizon. The estimated model focuses on Middle America in particular when making its predictions: providing insight into the benefit of employing spatial data.

Url: https://papers.ssrn.com/abstract=4350048

User Submitted?: No

Authors: Leslie, Jonathan

Series Title: CAEPR Working Paper

Publication Number: 2023-003

Institution: CAEPR

Pages: 1-21

Publisher Location:

Data Collections: IPUMS CPS

Topics: Population Mobility and Spatial Demography

Countries: