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Using LSTM Neural Networks for Nowcasting and Forecasting GVA of Industrial Sectors

Author

Listed:
  • Oleg Kryzhanovskiy

    (Bank of Russia; Tyumen State University)

  • Anastasia Mogilat

    (Bank of Russia)

  • Zhanna Shuvalova

    (Bank of Russia)

  • Dmitry Gvozdev

    (HSE University)

Abstract

This paper evaluates the potential application of long short-term memory (LSTM) neural networks for economic forecasting. We compare the accuracy of short-term forecasts of the gross value added of industrial sectors obtained using an LSTM model against several benchmarks, such as a random walk model, an autoregressive integrated moving average model, and an approximate dynamic factor model. Compared to the other models, the LSTM model demonstrates a lower mean absolute forecast error in 16 out of 18 cases and a lower root mean square error in 13 out of 18 cases.

Suggested Citation

  • Oleg Kryzhanovskiy & Anastasia Mogilat & Zhanna Shuvalova & Dmitry Gvozdev, 2025. "Using LSTM Neural Networks for Nowcasting and Forecasting GVA of Industrial Sectors," Russian Journal of Money and Finance, Bank of Russia, vol. 84(1), pages 93-104, March.
    • Handle: RePEc:bkr:journl:v:84:y:2025:i:1:p:93-104
    as

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    References listed on IDEAS

    as
    1. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    2. Urmat Dzhunkeev, 2024. "Forecasting Inflation in Russia Using Gradient Boosting and Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 83(1), pages 53-76, March.
    3. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    4. 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).
    5. Porshakov, A. & Ponomarenko, A. & Sinyakov, A., 2016. "Nowcasting and Short-Term Forecasting of Russian GDP with a Dynamic Factor Model," Journal of the New Economic Association, New Economic Association, vol. 30(2), pages 60-76.
    6. Evgeny Pavlov, 2020. "Forecasting Inflation in Russia Using Neural Networks," Russian Journal of Money and Finance, Bank of Russia, vol. 79(1), pages 57-73, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    GDP; GVA; neural networks; long short-term memory network; nowcasting; forecasting;
    All these keywords.

    JEL classification:

    • 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
    • C82 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Macroeconomic Data; Data Access
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General

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