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dc.contributor.authorClaveria González, Oscar
dc.contributor.authorMonte Moreno, Enrique
dc.contributor.authorTorra Porras, Salvador
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.date.accessioned2021-01-27T10:41:04Z
dc.date.available2021-01-27T10:41:04Z
dc.date.issued2021-01-18
dc.identifier.citationClaveria, O.; Monte, E.; Torra, S. A genetic programming approach for estimating economic sentiment in the Baltic countries and the European Union. "Technological and economic development of economy (Spausdinta)", 18 Gener 2021, vol. 27, núm. 1, p. 262-279.
dc.identifier.issn2029-4913
dc.identifier.urihttp://hdl.handle.net/2117/336076
dc.description.abstractIn this study, we introduce a sentiment construction method based on the evolution of survey-based indicators. We make use of genetic algorithms to evolve qualitative expectations in order to generate country-specific empirical economic sentiment indicators in the three Baltic republics and the European Union. First, for each country we search for the non-linear combination of firms’ and households’ expectations that minimises a fitness function. Second, we compute the frequency with which each survey expectation appears in the evolved indicators and examine the lag structure per variable selected by the algorithm. The industry survey indicator with the highest predictive performance are production expectations, while in the case of the consumer survey the distribution between variables is multi-modal. Third, we evaluate the out-of-sample predictive performance of the generated indicators, obtaining more accurate estimates of year-on-year GDP growth rates than with the scaled industrial and consumer confidence indicators. Finally, we use non-linear constrained optimisation to combine the evolved expectations of firms and consumers and generate aggregate expectations of of year-on-year GDP growth. We find that, in most cases, aggregate expectations outperform recursive autoregressive predictions of economic growth.
dc.description.sponsorshipThis work was supported by the Spanish Ministry of Science and Innovation under Grant PID2019-107579RB-I00.
dc.format.extent18 p.
dc.language.isoeng
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectÀrees temàtiques de la UPC::Economia i organització d'empreses
dc.subject.lcshGenetic algorithms
dc.subject.lcshEconomic forecasting
dc.subject.otherSentiment indicators
dc.subject.otherQualitative expectations
dc.subject.otherForecasting
dc.subject.otherEconomic growth
dc.titleA genetic programming approach for estimating economic sentiment in the Baltic countries and the European Union
dc.typeArticle
dc.subject.lemacAlgorismes genètics
dc.subject.lemacPrevisió econòmica
dc.contributor.groupUniversitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
dc.identifier.doi10.3846/tede.2021.13989
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://journals.vgtu.lt/index.php/TEDE/article/view/13989
dc.rights.accessOpen Access
local.identifier.drac30356312
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/AEI/2PE/PID2019-107579RB-I00
local.citation.authorClaveria, O.; Monte, E.; Torra, S.
local.citation.publicationNameTechnological and economic development of economy (Spausdinta)
local.citation.volume27
local.citation.number1
local.citation.startingPage262
local.citation.endingPage279


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Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution 4.0 Generic