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?Seeing? the Future: Improving Macroeconomic Forecasts with Spatial Data and Recurrent Convolutional Neural Networks

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  • Jonathan Leslie

    (Indiana University, Department of Economics)

Abstract

This paper presents a method of leveraging the information content of high dimensional, spatially-distributed economic data through borrowing techniques common in visual recognition artificial intelligence. Specifically, I cast spatially-disaggregated U.S. economic data as a sequence of quarterly geographic ?images? in a deep learning computer vision setting to evaluate whether leveraging the spatio-temporal distribution of predictors can improve macroeconomic forecasts. This spatial forecasting model produces highly-accurate out-of-sample forecasts of GDP (0.136 percentage point average mean absolute error (MAE)), inflation (0.066 percentage point average MAE), and industrial production (0.368 percentage point average MAE) across a four-quarter horizon. The model substantially outperforms both more traditional linear methods as well as deep learning methods that do not leverage the spatial distribution of the data.

Suggested Citation

  • Jonathan Leslie, 2023. "?Seeing? the Future: Improving Macroeconomic Forecasts with Spatial Data and Recurrent Convolutional Neural Networks," CAEPR Working Papers 2023-003 Classification-C, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
    • Handle: RePEc:inu:caeprp:2023003
    as

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    File URL: https://caepr.indiana.edu/RePEc/inu/caeprp/caepr2023-003.pdf
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    References listed on IDEAS

    as
    1. Livia Paranhos, 2021. "Predicting Inflation with Recurrent Neural Networks," Papers 2104.03757, arXiv.org, revised Oct 2023.
    2. Esther Rolf & Jonathan Proctor & Tamma Carleton & Ian Bolliger & Vaishaal Shankar & Miyabi Ishihara & Benjamin Recht & Solomon Hsiang, 2021. "A generalizable and accessible approach to machine learning with global satellite imagery," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    3. Li, Xuerong & Shang, Wei & Wang, Shouyang, 2019. "Text-based crude oil price forecasting: A deep learning approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1548-1560.
    4. Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023. "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1145-1162.
    5. Firat Melih Yilmaz & Ozer Arabaci, 2021. "Should Deep Learning Models be in High Demand, or Should They Simply be a Very Hot Topic? A Comprehensive Study for Exchange Rate Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 217-245, January.
    6. Zhao, Yang & Li, Jianping & Yu, Lean, 2017. "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, vol. 66(C), pages 9-16.
    7. Nakamura, Emi, 2005. "Inflation forecasting using a neural network," Economics Letters, Elsevier, vol. 86(3), pages 373-378, March.
    8. Patrick Bajari & Denis Nekipelov & Stephen P. Ryan & Miaoyu Yang, 2015. "Machine Learning Methods for Demand Estimation," American Economic Review, American Economic Association, vol. 105(5), pages 481-485, May.
    9. Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
    Full references (including those not matched with items on IDEAS)

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    Keywords

    Macroeconomic Forecasting; Machine Learning; Deep Learning; Computer Vision; Economic Geography;
    All these keywords.

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