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Macroeconomic Indicator Forecasting with Deep Neural Networks


  • Thomas Cook

    (Federal Reserve Bank of Kansas City)


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 and while remaining agnostic as to 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).

Suggested Citation

  • Thomas Cook, 2019. "Macroeconomic Indicator Forecasting with Deep Neural Networks," 2019 Meeting Papers 402, Society for Economic Dynamics.
  • Handle: RePEc:red:sed019:402

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    References listed on IDEAS

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    2. Nahapetyan Yervand, 2019. "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Sciendo, vol. 53(6), pages 286-303, January.
    3. Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020. "Deep Dynamic Factor Models," Papers 2007.11887,
    4. Goulet Coulombe, Philippe & Leroux, Maxime & Stevanovic, Dalibor & Surprenant, Stéphane, 2021. "Macroeconomic data transformations matter," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1338-1354.
    5. Suproteem K. Sarkar & Kojin Oshiba & Daniel Giebisch & Yaron Singer, 2018. "Robust Classification of Financial Risk," Papers 1811.11079,
    6. Rafael R. S. Guimaraes, 2022. "Deep Learning Macroeconomics," Papers 2201.13380,
    7. Tölö, Eero, 2020. "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, vol. 49(C).
    8. Richardson, Adam & van Florenstein Mulder, Thomas & Vehbi, Tuğrul, 2021. "Nowcasting GDP using machine-learning algorithms: A real-time assessment," International Journal of Forecasting, Elsevier, vol. 37(2), pages 941-948.
    9. Amir Mosavi & Pedram Ghamisi & Yaser Faghan & Puhong Duan, 2020. "Comprehensive Review of Deep Reinforcement Learning Methods and Applications in Economics," Papers 2004.01509,
    10. Alexey Bilgaev & Suocheng Dong & Fujia Li & Hao Cheng & Arnold Tulohonov & Erzhena Sadykova & Anna Mikheeva, 2020. "Baikal Region (Russia) Development Prospects Based on the Green Economy Principles," Sustainability, MDPI, vol. 13(1), pages 1-22, December.
    11. Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Research Discussion Papers 14/2019, Bank of Finland.
    12. Rodríguez-Vargas, Adolfo, 2020. "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 1(1).
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    14. Paranhos, Livia, 2021. "Predicting Inflation with Neural Networks," The Warwick Economics Research Paper Series (TWERPS) 1344, University of Warwick, Department of Economics.
    15. Denis Shibitov & Mariam Mamedli, 2021. "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series wps70, Bank of Russia.
    16. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    17. Marcelle Chauvet & Rafael R. S. Guimaraes, 2021. "Transfer Learning for Business Cycle Identification," Working Papers Series 545, Central Bank of Brazil, Research Department.
    18. Maehashi, Kohei & Shintani, Mototsugu, 2020. "Macroeconomic forecasting using factor models and machine learning: an application to Japan," Journal of the Japanese and International Economies, Elsevier, vol. 58(C).
    19. Kohei Maehashi & Mototsugu Shintani, 2020. "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series CIRJE-F-1146, CIRJE, Faculty of Economics, University of Tokyo.
    20. Anna Ter-Martirosyan & Manasa Patnam & Jin-Kyu Jung, 2018. "An Algorithmic Crystal Ball: Forecasts-based on Machine Learning," IMF Working Papers 2018/230, International Monetary Fund.
    21. Mihail Yanchev, 2022. "Deep Growth-at-Risk Model: Nowcasting the 2020 Pandemic Lockdown Recession in Small Open Economies," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 7, pages 20-41.
    22. Pedro Gerber Machado & Julia Tomei & Adam Hawkes & Celma de Oliveira Ribeiro, 2020. "A Simulator to Determine the Evolution of Disparities in Food Consumption between Socio-Economic Groups: A Brazilian Case Study," Sustainability, MDPI, vol. 12(15), pages 1-24, July.
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    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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