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September 10, 2025MALAYSIAN JOURNAL OF COMPUTINGOpen Access

Vulgarized Neighbouring Network of Multivariate Autoregressive Processes With Gaussian and Student-T Distributed Random Noises

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Authors

RORasaki Olawale OlanrewajuRRRavi RanjanQCQueensley C. Chukwudum

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Overview

Introduces a vulgarized network autoregressive method with student-t noise, suggesting improved forecasting outcomes over Gaussian noise.

Key Points

  • The GNAR model with student-t noise demonstrates superior model fitting, achieving a BIC of -39.2298 versus -18.1683 for Gaussian noise.
  • Residual errors indicate improved performance, with a student-t residual error of 0.9000 compared to 0.9900 for Gaussian.
  • Forecasting error metrics show a 15.105% reduction in MSE for the student-t model over the Gaussian counterpart.
  • The student-t GNAR model offers more symmetric residuals, enhancing estimation accuracy for time-varying prices.

Cite This Study

Olanrewaju et al. (2023) studied this question.

synapsesocial.com/papers/68c1e24854b1d3bfb60ff229https://doi.org/10.24191/mjoc.v8i2.23103
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