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Research on Small Sample Nonlinear Cointegration Test and Modeling Based on the LS‐SVM Optimized by PSO

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  • Jungang Du

Abstract

According to the definition of nonlinear cointegration, this article studies the small sample nonlinear cointegration test and NECM (Nonlinear Error Correction Model) based on the LS‐SVM (Least Squares Support Vector Machine) optimized by PSO (Particle Swarm Optimization). And the logical process of this method is also designed. Then, we carry out empirical research on the ship maintenance cost index and the several price indexes. Based on the judgment of the type of cointegration test relationship, the test of the nonlinear cointegration test relationship of small samples is realized, and the NECM for predicting the ship maintenance cost index is established as well, which is compared with the VAR model of linear vector. The research result shows that the small sample nonlinear cointegration test and modeling method based on the LS‐SVM Optimized by PSO can describe the nonlinear cointegration test relationship of the small sample system. And the NECM has better performance. The prediction effect can effectively predict small sample nonlinear systems. We also compare the prediction results with the wavelet neural network algorithm, and the results show that the generalization ability of LS‐SVM Optimized by PSO is better, and the prediction accuracy of small samples is higher.

Suggested Citation

  • Jungang Du, 2022. "Research on Small Sample Nonlinear Cointegration Test and Modeling Based on the LS‐SVM Optimized by PSO," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:8416706
    DOI: 10.1155/2022/8416706
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