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Application of the novel nonlinear grey Bernoulli model for forecasting unemployment rate

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  • Chen, Chun-I

Abstract

The grey model is characterized by basic mathematics and a need for less raw data, but it also lacks the flexibility to adjust the model to increase the precision for the forecasting model. This study investigates forecasting using novel nonlinear grey Bernoulli model (NGBM). The NGBM is a nonlinear differential equation with power n. The curvature of the solution curve can be adjusted according to the observed first time accumulated generating operation of raw data by properly choosing power n. The power n is determined using a simple computer program, which calculates the minimum average relative percentage error of the forecast model. The NGBM is applied to re-examine an example in Deng’s book and the analytical results demonstrate that it effectively enhances the modeling precision. The model precision can be increased owing to the nonlinearity of natural phenomena. The novel NGBM then is applied to forecast the annual unemployment rate of 10 selected countries for 2006. The modelling results help governments to develop future policies regarding labor and economic policies.

Suggested Citation

  • Chen, Chun-I, 2008. "Application of the novel nonlinear grey Bernoulli model for forecasting unemployment rate," Chaos, Solitons & Fractals, Elsevier, vol. 37(1), pages 278-287.
    • Handle: RePEc:eee:chsofr:v:37:y:2008:i:1:p:278-287
    DOI: 10.1016/j.chaos.2006.08.024
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    15. Liu, Liwei & Zong, Haijing & Zhao, Erdong & Chen, Chuxiang & Wang, Jianzhou, 2014. "Can China realize its carbon emission reduction goal in 2020: From the perspective of thermal power development," Applied Energy, Elsevier, vol. 124(C), pages 199-212.
    16. Yi-Chung Hu, 2022. "Demand forecasting of green metal materials using non-equidistant grey prediction with robust nonlinear interval regression analysis," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(8), pages 9809-9831, August.
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    23. Şahin, Utkucan & Ballı, Serkan & Chen, Yan, 2021. "Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods," Applied Energy, Elsevier, vol. 302(C).

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