Article (Scientific journals)
Latent Dirichlet Allocation Models for World Trade Analysis
Kozlowski, Diego; Semeshenko, Viktoriya; Molinari, Andrea
2021In PLoS ONE, 16 (2), p. 0245393
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Keywords :
COMTRADE data; Latent Dirichlet Allocation; Unsupervised Learning
Abstract :
[en] The international trade is one of the classic areas of study in economics. Nowadays, given the availability of data, the tools used for the analysis can be complemented and enriched with new methodologies and techniques that go beyond the traditional approach. The present paper shows the application of the Latent Dirichlet Allocation Models, a well known technique from the area of Natural Language Processing, to search for latent dimensions in the product space of international trade, and their distribution across countries over time. We apply this technique to a dataset of countries' exports of goods from 1962 to 2016. The findings show the possibility to generate higher level classifications of goods based on the empirical evidence, and also allow to study the distribution of those classifications within countries. The latter show interesting insights about countries' trade specialisation.
Disciplines :
Quantitative methods in economics & management
Author, co-author :
Kozlowski, Diego ;  University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Semeshenko, Viktoriya
Molinari, Andrea
External co-authors :
yes
Language :
English
Title :
Latent Dirichlet Allocation Models for World Trade Analysis
Publication date :
04 February 2021
Journal title :
PLoS ONE
ISSN :
1932-6203
Publisher :
Public Library of Science, San Franscisco, United States - California
Volume :
16
Issue :
2
Pages :
e0245393
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Computational Sciences
FnR Project :
FNR12252781 - Data-driven Computational Modelling And Applications, 2017 (01/09/2018-28/02/2025) - Andreas Zilian
Name of the research project :
DRIVEN
Funders :
FNR - Fonds National de la Recherche [LU]
Available on ORBilu :
since 15 December 2020

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