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Empirical modelling of survey-based expectations for the design of economic indicators in five European regions
dc.contributor.author | Claveria, 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 | 2018-01-16T14:34:28Z |
dc.date.available | 2019-01-09T01:30:30Z |
dc.date.issued | 2018-01-09 |
dc.identifier.citation | Claveria, O., Monte, E., Torra Porras, S. Empirical modelling of survey-based expectations for the design of economic indicators in five European regions. "Empirica", 9 Gener 2018, vol. 2018, p. 1-23. |
dc.identifier.issn | 0340-8744 |
dc.identifier.uri | http://hdl.handle.net/2117/112846 |
dc.description | This is a post-peer-review, pre-copyedit version of an article published in Empirica. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10663-017-9395-1”. |
dc.description.abstract | In this study we use agents’ expectations about the state of the economy to generate indicators of economic activity in twenty-six European countries grouped in five regions (Western, Eastern, and Southern Europe, and Baltic and Scandinavian countries). We apply a data-driven procedure based on evolutionary computation to transform survey variables in economic growth rates. In a first step, we design five independent experiments to derive a formula using survey variables that best replicates the evolution of economic growth in each region by means of genetic programming, limiting the integration schemes to the main mathematical operations. We then rank survey variables according to their performance in tracking economic activity, finding that agents’ ‘‘perception about the overall economy compared to last year’’ is the survey variable with the highest predictive power. In a second step, we assess the out-of-sample forecast accuracy of the evolved indicators. Although we obtain different results across regions, Austria, Slovakia, Portugal, Lithuania and Sweden are the economies of each region that show the best forecast results. We also find evidence that the forecasting performance of the survey-based indicators improves during periods of higher growth. |
dc.description.abstract | This is a post-peer-review, pre-copyedit version of an article published in Empirica. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10663-017-9395-1”. |
dc.format.extent | 23 p. |
dc.language.iso | eng |
dc.subject | Àrees temàtiques de la UPC::Economia i organització d'empreses |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Programació |
dc.subject.lcsh | Economic indicators |
dc.subject.lcsh | Genetic programming (Computer science) |
dc.subject.other | Economic indicators |
dc.subject.other | Qualitative survey data |
dc.subject.other | Expectations |
dc.subject.other | Symbolic regression |
dc.subject.other | Evolutionary algorithms |
dc.subject.other | Genetic programming |
dc.title | Empirical modelling of survey-based expectations for the design of economic indicators in five European regions |
dc.type | Article |
dc.subject.lemac | Indicadors econòmics |
dc.subject.lemac | Programació genètica (Informàtica) |
dc.contributor.group | Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
dc.identifier.doi | 10.1007/s10663-017-9395-1 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | https://link.springer.com/article/10.1007/s10663-017-9395-1 |
dc.rights.access | Open Access |
local.identifier.drac | 21710880 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//TEC2015-69266-P/ES/TECNOLOGIAS DE APRENDIZAJE PROFUNDO APLICADAS AL PROCESADO DE VOZ Y AUDIO/ |
local.citation.author | Claveria, O.; Monte, E.; Torra Porras, S. |
local.citation.publicationName | Empirica |
local.citation.startingPage | 1 |
local.citation.endingPage | 23 |
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