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Macroeconomic Indicator Forecasting with Deep Neural Networks

Author

Listed:
  • Cook, Thomas R.

    () (Federal Reserve Bank of Kansas City)

  • Smalter Hall, Aaron

    () (Federal Reserve Bank of Kansas City)

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).

Suggested Citation

  • Cook, Thomas R. & Smalter Hall, Aaron, 2017. "Macroeconomic Indicator Forecasting with Deep Neural Networks," Research Working Paper RWP 17-11, Federal Reserve Bank of Kansas City, revised 04 Sep 2017.
  • Handle: RePEc:fip:fedkrw:rwp17-11
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    File URL: https://doi.org/10.18651/RWP2017-11
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    References listed on IDEAS

    as
    1. Michael Creel, 2016. "Neural Nets for Indirect Inference," Working Papers 942, Barcelona Graduate School of Economics.
    2. Francis X. Diebold, 1998. "The Past, Present, and Future of Macroeconomic Forecasting," Journal of Economic Perspectives, American Economic Association, vol. 12(2), pages 175-192, Spring.
    3. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    4. Lucas, Robert Jr, 1976. "Econometric policy evaluation: A critique," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 1(1), pages 19-46, January.
    5. Pescatori, Andrea & Zaman, Saeed, 2011. "Macroeconomic models, forecasting, and policymaking," Economic Commentary, Federal Reserve Bank of Cleveland, issue Oct.
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    Cited by:

    1. Tölö, Eero, 2019. "Predicting systemic financial crises with recurrent neural networks," Research Discussion Papers 14/2019, Bank of Finland.
    2. Suproteem K. Sarkar & Kojin Oshiba & Daniel Giebisch & Yaron Singer, 2018. "Robust Classification of Financial Risk," Papers 1811.11079, arXiv.org.

    More about this item

    Keywords

    Neural networks; Forecasting; Macroeconomic indicators;

    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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