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dc.contributor.authorBoakye, Ernest Owusu
dc.contributor.authorHeimonen, Kari
dc.contributor.authorJunttila, Juha
dc.date.accessioned2024-05-27T05:52:24Z
dc.date.available2024-05-27T05:52:24Z
dc.date.issued2024
dc.identifier.citationBoakye, E. O., Heimonen, K., & Junttila, J. (2024). Commodity markets and the global macroeconomy : evidence from machine learning and GVAR. <i>Empirical Economics</i>, <i>Early online</i>. <a href="https://doi.org/10.1007/s00181-024-02612-0" target="_blank">https://doi.org/10.1007/s00181-024-02612-0</a>
dc.identifier.otherCONVID_215951938
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/95190
dc.description.abstractBased on a strongly data-intensive machine learning approach, this study first identifies the most essential globally traded commodities in view of their role for the global macroeconomic performance. At the second stage we estimate a global vector autoregressive model to assess in more detail these global reactions. Our results from the first stage indicate that of the 55 analyzed commodity markets, only four are revealed as the most important. At the second step, our GVAR analysis indicates that the commodity market effects on macroeconomic activity are neither unanimous across the commodities nor across macrovariables. As an overall result, the commodity market exposure is clearly stronger among the advanced countries such as the euro area, other developed economies, and China, compared to the emerging economies of Africa, Asia, and Latin America, at both the country and regional levels. This puts a lot of pressure on economic policies aimed at reducing, e.g., the depriving effects of commodity market price development on aggregate economic performance of these countries.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofseriesEmpirical Economics
dc.rightsCC BY 4.0
dc.subject.othercommodity prices
dc.subject.othermacroeconomy
dc.subject.othermachine learning
dc.subject.otherglobal VAR
dc.titleCommodity markets and the global macroeconomy : evidence from machine learning and GVAR
dc.typearticle
dc.identifier.urnURN:NBN:fi:jyu-202405273954
dc.contributor.laitosKauppakorkeakoulufi
dc.contributor.laitosSchool of Business and Economicsen
dc.type.urihttp://purl.org/eprint/type/JournalArticle
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.description.reviewstatuspeerReviewed
dc.relation.issn0377-7332
dc.relation.volumeEarly online
dc.type.versionpublishedVersion
dc.rights.copyright© The Author(s) 2024
dc.rights.accesslevelopenAccessfi
dc.subject.ysokoneoppiminen
dc.subject.ysokansainvälinen talous
dc.subject.ysomakrotalous
dc.subject.ysotalouspolitiikka
dc.format.contentfulltext
jyx.subject.urihttp://www.yso.fi/onto/yso/p21846
jyx.subject.urihttp://www.yso.fi/onto/yso/p16532
jyx.subject.urihttp://www.yso.fi/onto/yso/p26330
jyx.subject.urihttp://www.yso.fi/onto/yso/p978
dc.rights.urlhttps://creativecommons.org/licenses/by/4.0/
dc.relation.doi10.1007/s00181-024-02612-0
jyx.fundinginformationOpen Access funding provided by University of Oulu (including Oulu University Hospital). This project was supported by OP Financial Group Research Foundation, Finland, for the first author. [Under Grant Numbers 20210078].
dc.type.okmA1


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