Who will sign a Double Tax Treaty next? A prediction based on economic determinants and machine learning algorithms

19872 7 106936 2 324200 document 106936 1-s2.0-S0264999324001767-main.pdf application/pdf 738f1ee32832a1f49c500e2d2e3a816d MD5 5581004 2024-07-10 14:39:48 https://pure.iiasa.ac.at/id/eprint/19872/1/1-s2.0-S0264999324001767-main.pdf 19872 1 1 application/pdf text en public cc_by_4

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published 106937 1 324206 document 106937 indexcodes.txt text/x-c++ e722ae99a8839be863db4749985ed416 MD5 20524 2024-07-10 14:50:01 https://pure.iiasa.ac.at/id/eprint/19872/2/indexcodes.txt 19872 2 2 text/x-c++ other Generate index codes conversion from text to indexcodes en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/106936 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/106936 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/106936 archive 353 disk0/00/01/98/72 2024-07-10 14:41:11 2025-03-12 05:00:48 2024-07-10 14:41:11 article show Erokhin D. 9200 0000-0002-5191-0579 Zagler M. Who will sign a Double Tax Treaty next? A prediction based on economic determinants and machine learning algorithms pub ASA CAT Machine learning, Treaty formation, Double tax treaty Double tax treaties play a crucial role in shaping international economic relations, yet predicting which country pairs are likely to sign tax treaties remains a challenge. This study addresses this gap by employing a novel machine learning approach to predict tax treaty formations. Using data from a wide range of countries, we apply a series of classification algorithms and identify 59 country pairs likely to have tax treaties given their economic conditions. Our findings reveal that variables such as foreign direct investment, trade, Gross Domestic Product, and distance are significant predictors of tax treaty formations. Importantly, we demonstrate that the random forest classification algorithm outperforms conventional econometric methods in predicting tax treaty formations. By identifying which potential treaties exhibit a high probability of success, this paper gives policymakers an indication where to focus their attention and resources in upcoming treaty negotiations. 2024-10 published Pergamon Press doi:10.1016/j.econmod.2024.106819 3134 public Economic Modelling 139 e106819 TRUE 0264-9993 no info:eu-repo/semantics/article