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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.

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  • 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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    13. Pao, Hsiao-Tien & Fu, Hsin-Chia & Tseng, Cheng-Lung, 2012. "Forecasting of CO2 emissions, energy consumption and economic growth in China using an improved grey model," Energy, Elsevier, vol. 40(1), pages 400-409.
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    15. Adriana AnaMaria Davidescu & Simona-Andreea Apostu & Liviu Adrian Stoica, 2021. "Socioeconomic Effects of COVID-19 Pandemic: Exploring Uncertainty in the Forecast of the Romanian Unemployment Rate for the Period 2020–2023," Sustainability, MDPI, vol. 13(13), pages 1-22, June.
    16. Zheng-Xin Wang, 2017. "A Weighted Non-linear Grey Bernoulli Model for Forecasting Non-linear Economic Time Series with Small Data Sets," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(1), pages 169-186.
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    18. 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.
    19. Wen-Ze Wu & Tao Zhang & Chengli Zheng, 2019. "A Novel Optimized Nonlinear Grey Bernoulli Model for Forecasting China’s GDP," Complexity, Hindawi, vol. 2019, pages 1-10, October.
    20. Yin, Chen & Mao, Shuhua, 2023. "Fractional multivariate grey Bernoulli model combined with improved grey wolf algorithm: Application in short-term power load forecasting," Energy, Elsevier, vol. 269(C).
    21. Lao, Tongfei & Sun, Yanrui, 2022. "Predicting the production and consumption of natural gas in China by using a new grey forecasting method," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 202(C), pages 295-315.
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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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