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A genetic programming approach for estimating economic sentiment in the Baltic countries and the European Union
dc.contributor.author | Claveria González, Oscar |
dc.contributor.author | Monte Moreno, Enrique |
dc.contributor.author | Torra Porras, Salvador |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions |
dc.date.accessioned | 2021-01-27T10:41:04Z |
dc.date.available | 2021-01-27T10:41:04Z |
dc.date.issued | 2021-01-18 |
dc.identifier.citation | Claveria, 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.issn | 2029-4913 |
dc.identifier.uri | http://hdl.handle.net/2117/336076 |
dc.description.abstract | In 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.sponsorship | This work was supported by the Spanish Ministry of Science and Innovation under Grant PID2019-107579RB-I00. |
dc.format.extent | 18 p. |
dc.language.iso | eng |
dc.rights | Attribution 4.0 International |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ |
dc.subject | Àrees temàtiques de la UPC::Economia i organització d'empreses |
dc.subject.lcsh | Genetic algorithms |
dc.subject.lcsh | Economic forecasting |
dc.subject.other | Sentiment indicators |
dc.subject.other | Qualitative expectations |
dc.subject.other | Forecasting |
dc.subject.other | Economic growth |
dc.title | A genetic programming approach for estimating economic sentiment in the Baltic countries and the European Union |
dc.type | Article |
dc.subject.lemac | Algorismes genètics |
dc.subject.lemac | Previsió econòmica |
dc.contributor.group | Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
dc.identifier.doi | 10.3846/tede.2021.13989 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://journals.vgtu.lt/index.php/TEDE/article/view/13989 |
dc.rights.access | Open Access |
local.identifier.drac | 30356312 |
dc.description.version | Postprint (published version) |
dc.relation.projectid | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107579RB-I00/ES/ARQUITECTURAS AVANZADAS DE APRENDIZAJE PROFUNDO APLICADAS AL PROCESADO DE VOZ, AUDIO Y LENGUAJE/ |
local.citation.author | Claveria, O.; Monte, E.; Torra, S. |
local.citation.publicationName | Technological and economic development of economy (Spausdinta) |
local.citation.volume | 27 |
local.citation.number | 1 |
local.citation.startingPage | 262 |
local.citation.endingPage | 279 |
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