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Latent Dirichlet allocation model for world trade analysis.

Diego Kozlowski1, Viktoriya Semeshenko2,3, Andrea Molinari2,3

  • 1DRIVEN, FSTM, University of Luxembourg, Esch Sur Alzette, Luxembourg.

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This summary is machine-generated.

This study applies Latent Dirichlet allocation (LDA) to international trade data, revealing latent dimensions in product specialization. The method effectively captures country-specific trade patterns over time, offering a data-driven approach.

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Area of Science:

  • Economics
  • Data Science
  • Econometrics

Background:

  • Empirical analysis of international trade is complex due to vast product, country, and time data.
  • Traditional methods can be enhanced by new data-driven techniques for deeper insights.
  • A research gap exists in leveraging advanced methodologies for understanding trade phenomena.

Purpose of the Study:

  • To apply Latent Dirichlet allocation (LDA), a Natural Language Processing technique, to international trade data.
  • To identify latent dimensions within the product space of global trade.
  • To analyze the temporal distribution of these dimensions across countries.

Main Methods:

  • Latent Dirichlet allocation (LDA) model application.
  • Analysis of a comprehensive dataset of countries' exports of goods (1962-2016).
  • Examination of country-level specialization patterns and their evolution.

Main Results:

  • The LDA model successfully encodes key international trade specialization patterns.
  • The study visualizes changes in countries' trade specialization over time.
  • A unique, data-driven indicator is derived, simplifying the analysis of multiple trade phenomena.

Conclusions:

  • Latent Dirichlet allocation offers a powerful, data-driven complement to traditional international trade analysis tools.
  • The technique facilitates top-down studies by encoding complex trade dynamics into a single indicator.
  • This approach enhances the understanding of global trade specialization and its evolution.