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Artificial neural network regression models in a panel setting: Predicting economic growth

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  • Jahn, Malte

Abstract

Economic time series often feature non-linear structures such as non-linear time trends, non-linear autoregressive effects, and non-linear interaction effects. In this paper, it is shown that artificial neural network regression models are suitable tools for the analysis of economic panel data because they allow for a compromise between the ability to model these features and the model size. As model specification is a concern in artificial neural network models, previous approaches are discussed critically. It is shown that the growth rates of the gross domestic product of 24 industrialized economies in the period 1992–2016 follow a non-linear time trend which cannot be explained by autoregressive features or polynomial time variables. The unrestricted functional form of the time trend in the artificial neural network model is also the main reason for the superior statistical performance compared to conventional panel models. This is confirmed by out-of-sample predictions for 2017.

Suggested Citation

  • Jahn, Malte, 2020. "Artificial neural network regression models in a panel setting: Predicting economic growth," Economic Modelling, Elsevier, vol. 91(C), pages 148-154.
    • Handle: RePEc:eee:ecmode:v:91:y:2020:i:c:p:148-154
    DOI: 10.1016/j.econmod.2020.06.008
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    Cited by:

    1. Chou, Ping & Chuang, Howard Hao-Chun & Chou, Yen-Chun & Liang, Ting-Peng, 2022. "Predictive analytics for customer repurchase: Interdisciplinary integration of buy till you die modeling and machine learning," European Journal of Operational Research, Elsevier, vol. 296(2), pages 635-651.
    2. Alkhaleel, Basem A., 2024. "Machine learning applications in the resilience of interdependent critical infrastructure systems—A systematic literature review," International Journal of Critical Infrastructure Protection, Elsevier, vol. 44(C).
    3. Simon Blöthner & Mario Larch, 2022. "Economic determinants of regional trade agreements revisited using machine learning," Empirical Economics, Springer, vol. 63(4), pages 1771-1807, October.
    4. Malte Jahn, 2023. "Artificial neural networks and time series of counts: A class of nonlinear INGARCH models," Papers 2304.01025, arXiv.org.
    5. Jena, Pradyot Ranjan & Majhi, Ritanjali & Kalli, Rajesh & Managi, Shunsuke & Majhi, Babita, 2021. "Impact of COVID-19 on GDP of major economies: Application of the artificial neural network forecaster," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 324-339.
    6. Darko B. Vukovic & Lubov Spitsina & Ekaterina Gribanova & Vladislav Spitsin & Ivan Lyzin, 2023. "Predicting the Performance of Retail Market Firms: Regression and Machine Learning Methods," Mathematics, MDPI, vol. 11(8), pages 1-23, April.
    7. Mamadou Michel Diakhate & Seydi Ababacar Dieng, 2022. "Forecasting Senegalese quarterly GDP per capita using recurrent neural network," Economics Bulletin, AccessEcon, vol. 42(4), pages 1874-1887.
    8. Malte Jahn, 2023. "Regressing on distributions: The nonlinear effect of temperature on regional economic growth," Papers 2309.10481, arXiv.org.

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    More about this item

    Keywords

    Neural networks; Forecasting; Panel data;
    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
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • O40 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - General

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