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A Machine Learning Approach to Forecast International Trade: The Case of Croatia

Author

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  • Jošić Hrvoje

    (University of Zagreb, Faculty of Economics and Business, Zagreb, Croatia)

  • Žmuk Berislav

    (University of Zagreb, Faculty of Economics and Business, Zagreb, Croatia)

Abstract

Background: This paper presents a machine learning approach to forecast Croatia’s international bilateral trade. Objectives: The goal of this paper is to evaluate the performance of machine learning algorithms in predicting international bilateral trade flows related to imports and exports in the case of Croatia. Methods/Approach: The dataset on Croatian bilateral trade with over 180 countries worldwide from 2001 to 2019 is assembled using main variables from the gravity trade model. To forecast values of Croatian bilateral exports and imports for a horizon of one year (the year 2020), machine learning algorithms (Gaussian processes, Linear regression, and Multilayer perceptron) have been used. Each forecasting algorithm is evaluated by calculating mean absolute percentage errors (MAPE). Results: It was found that machine learning algorithms have a very good predicting ability in forecasting Croatian bilateral trade, with neural network Multilayer perceptron having the best performance among the other machine learning algorithms. Conclusions Main findings from this paper can be important for economic policymakers and other subjects in this field of research. Timely information about the changes in trends and projections of future trade flows can significantly affect decision-making related to international bilateral trade flows.

Suggested Citation

  • Jošić Hrvoje & Žmuk Berislav, 2022. "A Machine Learning Approach to Forecast International Trade: The Case of Croatia," Business Systems Research, Sciendo, vol. 13(3), pages 144-160, October.
    • Handle: RePEc:bit:bsrysr:v:13:y:2022:i:3:p:144-160:n:2
    DOI: 10.2478/bsrj-2022-0030
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    References listed on IDEAS

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    5. Avi Goldfarb & Daniel Trefler, 2018. "Artificial Intelligence and International Trade," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 463-492, National Bureau of Economic Research, Inc.
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    More about this item

    Keywords

    machine learning; WEKA; international trade; MAPE; Multilayer perceptron; Croatia;
    All these keywords.

    JEL classification:

    • B17 - Schools of Economic Thought and Methodology - - History of Economic Thought through 1925 - - - International Trade and Finance
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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