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A Novel Grey Wave Method for Predicting Total Chinese Trade Volume

Author

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  • Kedong Yin

    (School of Economics, Ocean University of China, Qingdao 266100, China
    Ocean Development Research Institute, Major Research Base of Humanities and Social Sciences of Ministry of Education, Ocean University of China, Qingdao 266100, China)

  • Danning Lu

    (School of Economics, Ocean University of China, Qingdao 266100, China)

  • Xuemei Li

    (School of Economics, Ocean University of China, Qingdao 266100, China
    Ocean Development Research Institute, Major Research Base of Humanities and Social Sciences of Ministry of Education, Ocean University of China, Qingdao 266100, China)

Abstract

The total trade volume of a country is an important way of appraising its international trade situation. A prediction based on trade volume will help enterprises arrange production efficiently and promote the sustainability of the international trade. Because the total Chinese trade volume fluctuates over time, this paper proposes a Grey wave forecasting model with a Hodrick–Prescott filter (HP filter) to forecast it. This novel model first parses time series into long-term trend and short-term cycle. Second, the model uses a general GM (1,1) to predict the trend term and the Grey wave forecasting model to predict the cycle term. Empirical analysis shows that the improved Grey wave prediction method provides a much more accurate forecast than the basic Grey wave prediction method, achieving better prediction results than autoregressive moving average model (ARMA).

Suggested Citation

  • Kedong Yin & Danning Lu & Xuemei Li, 2017. "A Novel Grey Wave Method for Predicting Total Chinese Trade Volume," Sustainability, MDPI, vol. 9(12), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:12:p:2367-:d:123389
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    References listed on IDEAS

    as
    1. Chen, Yanhui & He, Kaijian & Zhang, Chuan, 2016. "A novel grey wave forecasting method for predicting metal prices," Resources Policy, Elsevier, vol. 49(C), pages 323-331.
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    3. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    4. James D. Hamilton, 2017. "Why You Should Never Use the Hodrick-Prescott Filter," NBER Working Papers 23429, National Bureau of Economic Research, Inc.
    5. Min-Chun Yu & Chia-Nan Wang & Nguyen-Nhu-Y Ho, 2016. "A Grey Forecasting Approach for the Sustainability Performance of Logistics Companies," Sustainability, MDPI, vol. 8(9), pages 1-18, August.
    6. Bai, Chunguang & Sarkis, Joseph, 2010. "Integrating sustainability into supplier selection with grey system and rough set methodologies," International Journal of Production Economics, Elsevier, vol. 124(1), pages 252-264, March.
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    Cited by:

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    2. Petr Suler & Zuzana Rowland & Tomas Krulicky, 2021. "Evaluation of the Accuracy of Machine Learning Predictions of the Czech Republic’s Exports to the China," JRFM, MDPI, vol. 14(2), pages 1-30, February.

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