IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v9y2017i12p2367-d123389.html
   My bibliography  Save this article

A Novel Grey Wave Method for Predicting Total Chinese Trade Volume

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/9/12/2367/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/9/12/2367/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hodrick, Robert J & Prescott, Edward C, 1997. "Postwar U.S. Business Cycles: An Empirical Investigation," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 29(1), pages 1-16, February.
    2. 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.
    3. 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.
    4. 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.
    5. James D. Hamilton, 2017. "Why You Should Never Use the Hodrick-Prescott Filter," NBER Working Papers 23429, National Bureau of Economic Research, Inc.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Saeed, Naima & Nguyen, Su & Cullinane, Kevin & Gekara, Victor & Chhetri, Prem, 2023. "Forecasting container freight rates using the Prophet forecasting method," Transport Policy, Elsevier, vol. 133(C), pages 86-107.
    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nataliia Ostapenko, 2022. "Do output gap estimates improve inflation forecasts in Slovakia?," Working and Discussion Papers WP 4/2022, Research Department, National Bank of Slovakia.
    2. Vitek, Francis, 2006. "Measuring the Stance of Monetary Policy in a Small Open Economy: A Dynamic Stochastic General Equilibrium Approach," MPRA Paper 802, University Library of Munich, Germany.
    3. Joseph P. Byrne & Dimitris Korobilis & Pinho J. Ribeiro, 2018. "On The Sources Of Uncertainty In Exchange Rate Predictability," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 59(1), pages 329-357, February.
    4. Matthieu LEMOINE & Odile CHAGNY, 2005. "Estimating the potential output of the euro area with a semi-structural multivariate Hodrick-Prescott filter," Computing in Economics and Finance 2005 344, Society for Computational Economics.
    5. Yun, Jaeho, 2019. "Bond risk premia in a small open economy with volatile capital flows: The case of Korea," Journal of International Money and Finance, Elsevier, vol. 93(C), pages 223-243.
    6. Joseph Agyapong, 2021. "Application of Taylor Rule Fundamentals in Forecasting Exchange Rates," Economies, MDPI, vol. 9(2), pages 1-27, June.
    7. Kwas, Marek & Paccagnini, Alessia & Rubaszek, Michał, 2021. "Common factors and the dynamics of industrial metal prices. A forecasting perspective," Resources Policy, Elsevier, vol. 74(C).
    8. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting: Part II — Probabilistic forecasting," Energy Economics, Elsevier, vol. 79(C), pages 171-182.
    9. Wolfgang Nierhaus & Timo Wollmershäuser, 2016. "ifo Konjunkturumfragen und Konjunkturanalyse: Band II," ifo Forschungsberichte, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 72.
    10. Wojnilower, Joshua, 2018. "On credit and output: Is the supply of credit relevant?," The North American Journal of Economics and Finance, Elsevier, vol. 45(C), pages 38-56.
    11. McKnight, Stephen & Mihailov, Alexander & Rumler, Fabio, 2020. "Inflation forecasting using the New Keynesian Phillips Curve with a time-varying trend," Economic Modelling, Elsevier, vol. 87(C), pages 383-393.
    12. Patnaik, Ila & Mittal, Shalini & Pandey, Radhika, 2019. "Examining the trade-off between price and financial stability in India," Working Papers 19/248, National Institute of Public Finance and Policy.
    13. Klein, Tony, 2018. "Trends and contagion in WTI and Brent crude oil spot and futures markets - The role of OPEC in the last decade," Energy Economics, Elsevier, vol. 75(C), pages 636-646.
    14. Karamé, Frédéric & Patureau, Lise & Sopraseuth, Thepthida, 2008. "Limited participation and exchange rate dynamics: Does theory meet the data?," Journal of Economic Dynamics and Control, Elsevier, vol. 32(4), pages 1041-1087, April.
    15. Norman Swanson & Oleg Korenok, 2006. "How Sticky Is Sticky Enough? A Distributional and Impulse Response Analysis of New Keynesian DSGE Models. Extended Working Paper Version," Departmental Working Papers 200612, Rutgers University, Department of Economics.
    16. Ataman Ozyildirim & Brian Schaitkin & Victor Zarnowitz, 2010. "Business cycles in the euro area defined with coincident economic indicators and predicted with leading economic indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(1-2), pages 6-28.
    17. Jon Danielsson & Marcela Valenzuela & Ilknur Zer, 2018. "Learning from History: Volatility and Financial Crises," The Review of Financial Studies, Society for Financial Studies, vol. 31(7), pages 2774-2805.
    18. Hans-Eggert Reimers, 2003. "Does Money Include Information for Output in the Euro Area?," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 139(II), pages 231-252, June.
    19. Bofinger, Peter & Schnabel, Isabel & Feld, Lars P. & Schmidt, Christoph M. & Wieland, Volker, 2017. "Für eine zukunftsorientierte Wirtschaftspolitik. Jahresgutachten 2017/18 [Towards a Forward-Looking Economic Policy. Annual Report 2017/18]," Annual Economic Reports / Jahresgutachten, German Council of Economic Experts / Sachverständigenrat zur Begutachtung der gesamtwirtschaftlichen Entwicklung, volume 127, number 201718.
    20. Usman Zafar & Neil Kellard & Dmitri Vinogradov, 2022. "Multistage optimization filter for trend‐based short‐term forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 345-360, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:9:y:2017:i:12:p:2367-:d:123389. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.