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Economic Growth Prediction Using Optimized Support Vector Machines

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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.

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Authors and Affiliations

  1. Damghan Branch, Islamic Azad University Iran, Damghan, Iran

    Elmira Emsia

  2. Economics and Business Department Famagusta, Eastern Mediterranean University (EMU), Famagusta, Cyprus

    Cagay Coskuner

Authors
  1. Elmira Emsia
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  2. Cagay Coskuner
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Correspondence to Elmira Emsia.

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Emsia, E., Coskuner, C. Economic Growth Prediction Using Optimized Support Vector Machines. Comput Econ 48, 453–462 (2016). https://doi.org/10.1007/s10614-015-9528-1

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  • DOI: https://doi.org/10.1007/s10614-015-9528-1

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