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dc.contributor.authorClaveria, 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.identifier.citationClaveria, O., Monte, E., Torra Porras, S. A data-driven approach to construct survey-based indicators by means of evolutionary algorithms. "Social indicators research", 9 Gener 2018.
dc.descriptionThe final publication is available at Springer via
dc.description.abstractIn this paper we propose a data-driven approach for the construction of survey-based indicators using large data sets. We make use of agents’ expectations about a wide range of economic variables contained in the World Economic Survey, which is a tendency survey conducted by the Ifo Institute for Economic Research. By means of genetic programming we estimate a symbolic regression that links survey-based expectations to a quantitative variable used as a yardstick, deriving mathematical functional forms that approximate the target variable. We use the evolution of GDP as a target. This set of empirically-generated indicators of economic growth, are used as building blocks to construct an economic indicator. We compare the proposed indicator to the Economic Climate Index, and we evaluate its predictive performance to track the evolution of the GDP in ten European economies. We find that in most countries the proposed indicator outperforms forecasts generated by a benchmark model.
dc.subjectÀrees temàtiques de la UPC::Economia i organització d'empreses
dc.subjectÀrees temàtiques de la UPC::Informàtica::Programació
dc.subject.lcshEconomic indicators
dc.subject.lcshGenetic programming (Computer science)
dc.subject.otherEconomic indicators
dc.subject.otherSurvey-based indicators
dc.subject.otherTendency surveys
dc.subject.otherSymbolic regression
dc.subject.otherEvolutionary algorithms
dc.subject.otherGenetic programming
dc.titleA data-driven approach to construct survey-based indicators by means of evolutionary algorithms
dc.subject.lemacIndicadors econòmics
dc.subject.lemacProgramació genètica (Informàtica)
dc.contributor.groupUniversitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (author's final draft)
upcommons.citation.authorClaveria, O., Monte, E., Torra Porras, S.
upcommons.citation.publicationNameSocial indicators research

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