IDEAS home Printed from https://ideas.repec.org/a/aid/journl/v5y2022i2p7-23.html
   My bibliography  Save this article

The Dynamics of Trade Relations between Ukraine and Romania: Modelling and Forecasting

Author

Listed:
  • Oleksandr Melnychenko

    (Gdansk University of Technology, Gdansk, Poland)

  • Valerii Matskul

    (Odesa National Economic University, Odesa, Ukraine)

  • Tetiana Osadcha

    (Odesa Mechnikov National University, Odesa, Ukraine)

Abstract

The article examines the monthly dynamics of exports, imports and balance of trade between Ukraine and Romania in the period from 2005 to 2021. Time series from 2015 to 2021 were used for modelling and forecasting (since the date the European Union–Ukraine Association Agreement took effect). Adequate models of the dynamics series of the Box-Jenkins methodology were built: additive models with seasonal component ARIMA (Autoregressive Integrated Moving Average) ARIMAS (or SARIMA) and Holt-Winters exponential smoothing with a dampened trend. Forecasting of exports, imports and trade balance for the fourth quarter of 2021 and first quarter of 2022 were completed. The forecast results showed a small relative error compared to the actual data. Thus, when forecasting the trade balance between countries using the Holt-Winters model, the relative prediction errors were: for October 2021 – 1.3%; for November 2021 – 2.6%; for December 2021 – 0.4%.

Suggested Citation

  • Oleksandr Melnychenko & Valerii Matskul & Tetiana Osadcha, 2022. "The Dynamics of Trade Relations between Ukraine and Romania: Modelling and Forecasting," Virtual Economics, The London Academy of Science and Business, vol. 5(2), pages 7-23, July.
    • Handle: RePEc:aid:journl:v:5:y:2022:i:2:p:7-23
    DOI: 10.34021/ve.2022.05.02(1)
    as

    Download full text from publisher

    File URL: https://virtual-economics.eu/index.php/VE/article/download/181/102
    Download Restriction: no
    File URL: https://libkey.io/10.34021/ve.2022.05.02(1)?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yang Chen & Aleksy Kwilinski & Olena Chygryn & Oleksii Lyulyov & Tetyana Pimonenko, 2021. "The Green Competitiveness of Enterprises: Justifying the Quality Criteria of Digital Marketing Communication Channels," Sustainability, MDPI, vol. 13(24), pages 1-13, December.
    2. Peter R. Winters, 1960. "Forecasting Sales by Exponentially Weighted Moving Averages," Management Science, INFORMS, vol. 6(3), pages 324-342, April.
    3. Mazaher Kianpour & Stewart J. Kowalski & Harald Øverby, 2021. "Systematically Understanding Cybersecurity Economics: A Survey," Sustainability, MDPI, vol. 13(24), pages 1-28, December.
    4. Dalia STREIMIKIENE & Rizwan Raheem AHMED & Saghir Pervaiz GHAURI & Muhammad AQIL & Justas STREIMIKIS, 2020. "Forecasting and the Causal Relationship of Sectorial Energy Consumptions and GDP of Pakistan by using AR, ARIMA, and Toda-Yamamoto Wald Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 131-148, July.
    5. Aleksandra Kuzior & Aleksy Kwilinski & Ihor Hroznyi, 2021. "The Factorial-Reflexive Approach to Diagnosing the Executors’ and Contractors’ Attitude to Achieving the Objectives by Energy Supplying Companies," Energies, MDPI, vol. 14(9), pages 1-16, April.
    Full references (including those not matched with items on IDEAS)

    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. Radoslaw Miskiewicz, 2022. "Clean and Affordable Energy within Sustainable Development Goals: The Role of Governance Digitalization," Energies, MDPI, vol. 15(24), pages 1-17, December.
    2. Oleksii Lyulyov & Olena Chygryn & Tetyana Pimonenko & Aleksy Kwilinski, 2023. "Stakeholders’ Engagement in the Company’s Management as a Driver of Green Competitiveness within Sustainable Development," Sustainability, MDPI, vol. 15(9), pages 1-15, April.
    3. A A Syntetos & J E Boylan & S M Disney, 2009. "Forecasting for inventory planning: a 50-year review," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(1), pages 149-160, May.
    4. P. A. Nazarov & Kazakova, Maria, 2014. "Theoretical Basis of Prediction of Main Budget Parameters of Country," Published Papers r90221, Russian Presidential Academy of National Economy and Public Administration.
    5. Svetunkov, Ivan & Chen, Huijing & Boylan, John E., 2023. "A new taxonomy for vector exponential smoothing and its application to seasonal time series," European Journal of Operational Research, Elsevier, vol. 304(3), pages 964-980.
    6. Vesna Karadzic & Bojan Pejovic, 2021. "Inflation Forecasting in the Western Balkans and EU: A Comparison of Holt-Winters, ARIMA and NNAR Models," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, vol. 23(57), pages 517-517.
    7. Segura, J. V. & Vercher, E., 2001. "A spreadsheet modeling approach to the Holt-Winters optimal forecasting," European Journal of Operational Research, Elsevier, vol. 131(2), pages 375-388, June.
    8. Li, Qinyun & Gaalman, Gerard & Disney, Stephen M., 2023. "On the equivalence of the proportional and damped trend order-up-to policies: An eigenvalue analysis," International Journal of Production Economics, Elsevier, vol. 265(C).
    9. Hyunjin Lee & Taesik Lee, 2021. "Demand modelling for emergency medical service system with multiple casualties cases: k-inflated mixture regression model," Flexible Services and Manufacturing Journal, Springer, vol. 33(4), pages 1090-1115, December.
    10. Shahriyar Mukhtarov & Hasan Dinçer & Halim Baş & Serhat Yüksel, 2022. "Policy Recommendations for Handling Brain Drains to Provide Sustainability in Emerging Economies," Sustainability, MDPI, vol. 14(23), pages 1-24, December.
    11. Denafas, Gintaras & Ruzgas, Tomas & Martuzevičius, Dainius & Shmarin, Sergey & Hoffmann, Michael & Mykhaylenko, Valeriy & Ogorodnik, Stanislav & Romanov, Mikhail & Neguliaeva, Ekaterina & Chusov, Alex, 2014. "Seasonal variation of municipal solid waste generation and composition in four East European cities," Resources, Conservation & Recycling, Elsevier, vol. 89(C), pages 22-30.
    12. Dinis, Duarte & Barbosa-Póvoa, Ana & Teixeira, Ângelo Palos, 2022. "Enhancing capacity planning through forecasting: An integrated tool for maintenance of complex product systems," International Journal of Forecasting, Elsevier, vol. 38(1), pages 178-192.
    13. Jiyoung Park & James E. Moore & Peter Gordon & Harry W. Richardson, 2017. "A New Approach to Quantifying the Impact of Hurricane-Disrupted Oil Refinery Operations Utilizing Secondary Data," Group Decision and Negotiation, Springer, vol. 26(6), pages 1125-1144, November.
    14. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    15. Zhong, Ziqi & Zhao, Elena Yifei, 2024. "Collaborative driving mode of sustainable marketing and supply chain management supported by metaverse technology," LSE Research Online Documents on Economics 121160, London School of Economics and Political Science, LSE Library.
    16. Rajapaksha, Dilini & Bergmeir, Christoph & Hyndman, Rob J., 2023. "LoMEF: A framework to produce local explanations for global model time series forecasts," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1424-1447.
    17. Villegas, Marco A. & Pedregal, Diego J., 2019. "Automatic selection of unobserved components models for supply chain forecasting," International Journal of Forecasting, Elsevier, vol. 35(1), pages 157-169.
    18. Jennifer Castle & David Hendry, 2012. "Forecasting by factors, by variables, or both?," Economics Series Working Papers 600, University of Oxford, Department of Economics.
    19. Niels Haldrup & Oskar Knapik & Tommaso Proietti, 2016. "A generalized exponential time series regression model for electricity prices," CREATES Research Papers 2016-08, Department of Economics and Business Economics, Aarhus University.
    20. Koehler, Anne B. & Snyder, Ralph D. & Ord, J. Keith, 2001. "Forecasting models and prediction intervals for the multiplicative Holt-Winters method," International Journal of Forecasting, Elsevier, vol. 17(2), pages 269-286.

    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:aid:journl:v:5:y:2022:i:2:p:7-23. 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: Aleksy Kwilinski (email available below). General contact details of provider: https://edirc.repec.org/data/akwilin.html .

    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.