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A Neural Network-VAR for Long-Term Forecasting: An Application to Monetary Policy Effects in the Euro Area

Author

Listed:
  • Diana Barro

    (Ca’ Foscari University of Venice)

  • Antonella Basso

    (Ca’ Foscari University of Venice)

  • Marco Corazza

    (Ca’ Foscari University of Venice)

  • Guglielmo Alessandro Visentin

    (Henley Business School, University of Reading)

Abstract

We propose a hybrid approach that combines Neural Networks with a Vector Autoregression (VAR) model to generate long-term forecasts of time series. We apply this methodology to forecast the impact of shifts in monetary policies within the Euro area on a comprehensive set of macroeconomic variables. Our analysis begins with a standard (linear) VAR model, which is then enhanced by incorporating Neural Networks to generate long-term forecasts for key variables such as the interest rate, inflation, real output, narrow money, exchange rate, and corporate bond spread. The results suggest that a Neural Network-VAR model offers improvements over the traditional linear VAR for forecasting certain macroeconomic variables in the long run. However, due to the limited sample size, the nonlinear model does not consistently outperform the linear VAR.

Suggested Citation

  • Diana Barro & Antonella Basso & Marco Corazza & Guglielmo Alessandro Visentin, 2025. "A Neural Network-VAR for Long-Term Forecasting: An Application to Monetary Policy Effects in the Euro Area," Working Papers 2025: 24, Department of Economics, University of Venice "Ca' Foscari".
    • Handle: RePEc:ven:wpaper:2025:24
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    References listed on IDEAS

    as
    1. 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.
    2. Geiger, Martin & Güntner, Jochen, 2024. "The chronology of Brexit and UK monetary policy," Journal of Monetary Economics, Elsevier, vol. 142(C).
    3. Anders Bredahl Kock & Timo Teräsvirta, 2016. "Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1753-1779, December.
    4. Joseph, Andreas & Potjagailo, Galina & Chakraborty, Chiranjit & Kapetanios, George, 2024. "Forecasting UK inflation bottom up," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1521-1538.
    5. Harald Badinger & Stefan Schiman, 2023. "Measuring Monetary Policy in the Euro Area Using SVARs with Residual Restrictions," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(2), pages 279-305, April.
    6. Jaehyun Yoon, 2021. "Forecasting of Real GDP Growth Using Machine Learning Models: Gradient Boosting and Random Forest Approach," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 247-265, January.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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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
    • 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
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy

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