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Nowcasting US GDP with artificial neural networks

Author

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  • Loermann, Julius
  • Maas, Benedikt

Abstract

We use a machine learning approach to forecast the US GDP value of the current quarter and several quarters ahead. Within each quarter, the contemporaneous value of GDP growth is unavailable but can be estimated using higher-frequency variables that are published in a more timely manner. Using the monthly FRED-MD database, we compare the feedforward artificial neural network forecasts of GDP growth to forecasts of state of the art dynamic factor models and the Survey of Professional Forecasters, and we evaluate the relative performance. The results indicate that the neural network outperforms the dynamic factor model in terms of now- and forecasting, while it generates at least as good now- and forecasts as the Survey of Professional Forecasters.

Suggested Citation

  • Loermann, Julius & Maas, Benedikt, 2019. "Nowcasting US GDP with artificial neural networks," MPRA Paper 95459, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:95459
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    References listed on IDEAS

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    1. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
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    6. Domenico Giannone & Lucrezia Reichlin & David Small, 2008. "Nowcasting: the real time informational content of macroeconomic data releases," ULB Institutional Repository 2013/6409, ULB -- Universite Libre de Bruxelles.
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    10. Tkacz, Greg, 2001. "Neural network forecasting of Canadian GDP growth," International Journal of Forecasting, Elsevier, vol. 17(1), pages 57-69.
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    Citations

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    Cited by:

    1. Longo, Luigi & Riccaboni, Massimo & Rungi, Armando, 2022. "A neural network ensemble approach for GDP forecasting," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    2. Paolo Andreini & Cosimo Izzo & Giovanni Ricco, 2020. "Deep Dynamic Factor Models," Papers 2007.11887, arXiv.org, revised May 2023.
    3. Daniel Hopp, 2022. "Benchmarking Econometric and Machine Learning Methodologies in Nowcasting," Papers 2205.03318, arXiv.org.
    4. Barış Soybilgen & Ege Yazgan, 2021. "Nowcasting US GDP Using Tree-Based Ensemble Models and Dynamic Factors," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 387-417, January.
    5. Maas, Benedikt, 2019. "Nowcasting and forecasting US recessions: Evidence from the Super Learner," MPRA Paper 96408, University Library of Munich, Germany.
    6. Krist'of N'emeth & D'aniel Hadh'azi, 2023. "GDP nowcasting with artificial neural networks: How much does long-term memory matter?," Papers 2304.05805, arXiv.org, revised Jan 2025.
    7. Daniel Hopp, 2021. "Economic Nowcasting with Long Short-Term Memory Artificial Neural Networks (LSTM)," Papers 2106.08901, arXiv.org.
    8. Daniel Hopp, 2022. "Performance of long short-term memory artificial neural networks in nowcasting during the COVID-19 crisis," Papers 2203.11872, arXiv.org.
    9. Daniel Hopp, 2024. "Benchmarking econometric and machine learning methodologies in nowcasting GDP," Empirical Economics, Springer, vol. 66(5), pages 2191-2247, May.
    10. Iva Glišic, 2024. "A comparison of using MIDAS and LSTM models for GDP nowcasting," Working Papers Bulletin 22, National Bank of Serbia.

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    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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