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Economic Growth Prediction Using Optimized Support Vector Machines

Author

Listed:
  • Elmira Emsia

    (Islamic Azad University Iran)

  • Cagay Coskuner

    (Eastern Mediterranean University (EMU))

Abstract

The main objective of this research is to propose a new hybrid model called genetic algorithms–support vector regression (GA–SVR). The proposed model consists of three stages. In the first stage, after lag selection, the most efficient features are selected using stepwise regression algorithm (SRA). Afterward, these variables are used in order to develop proposed model, in which the model uses support vector machines that the parameters of which are tuned by GA. Finally, evaluation of the proposed model is carried out by applying it on the test data set.

Suggested Citation

  • Elmira Emsia & Cagay Coskuner, 2016. "Economic Growth Prediction Using Optimized Support Vector Machines," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 453-462, October.
    • Handle: RePEc:kap:compec:v:48:y:2016:i:3:d:10.1007_s10614-015-9528-1
    DOI: 10.1007/s10614-015-9528-1
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    Cited by:

    1. Juan Laborda & Sonia Ruano & Ignacio Zamanillo, 2023. "Multi-Country and Multi-Horizon GDP Forecasting Using Temporal Fusion Transformers," Mathematics, MDPI, vol. 11(12), pages 1-26, June.
    2. Xiaohan Xu & Roy Anthony Rogers & Mario Arturo Ruiz Estrada, 2023. "A Novel Prediction Model: ELM-ABC for Annual GDP in the Case of SCO Countries," Computational Economics, Springer;Society for Computational Economics, vol. 62(4), pages 1545-1566, December.
    3. Mostafaei, Kamran & maleki, Shaho & Zamani Ahmad Mahmoudi, Mohammad & Knez, Dariusz, 2022. "Risk management prediction of mining and industrial projects by support vector machine," Resources Policy, Elsevier, vol. 78(C).
    4. 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.

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