A new approach for the quantification of qualitative measures of economic expectations

In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents' expectations. The research focuses on experts' expectations abou...

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Detalles Bibliográficos
Autores: Clavería González, Óscar, Monte Moreno, Enric, Torra Porras, Salvador
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2016
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:2445/117334
Acceso en línea:https://hdl.handle.net/2445/117334
Access Level:acceso abierto
Palabra clave:Creixement econòmic
Investigació qualitativa
Anàlisi de regressió
Algorismes genètics
Economic growth
Qualitative research
Regression analysis
Genetic algorithms
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spelling A new approach for the quantification of qualitative measures of economic expectationsClavería González, ÓscarMonte Moreno, EnricTorra Porras, SalvadorCreixement econòmicInvestigació qualitativaAnàlisi de regressióAlgorismes genèticsEconomic growthQualitative researchRegression analysisGenetic algorithmsIn this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents' expectations. The research focuses on experts' expectations about the state of the economy from the World Economic Survey in twenty eight countries of the Organisation for Economic Co-operation and Development. The proposed method is used to transform qualitative responses into estimates of economic growth. In a first experiment, we combine agents' expectations about the future to construct a leading indicator of economic activity. In a second experiment, agents' judgements about the present are combined to generate a coincident indicator. Then, we use index tracking to derive the optimal combination of weights for both indicators that best replicates the evolution of economic activity in each country. Finally, we compute several accuracy measures to assess the performance of these estimates in tracking economic growth. The different results across countries have led us to use multidimensional scaling analysis in order to group all economies in four clusters according to their performance.Springer Verlag2017201720162017info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://hdl.handle.net/2445/117334Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésVersió postprint del document publicat a: https://doi.org/10.1007/s11135-016-0416-0Quality & Quantity, 2016, vol. 51, num. 6, p. 2685-2706https://doi.org/10.1007/s11135-016-0416-0(c) Springer Verlag, 2016info:eu-repo/semantics/openAccessoai:recercat.cat:2445/1173342026-05-29T05:05:01Z
dc.title.none.fl_str_mv A new approach for the quantification of qualitative measures of economic expectations
title A new approach for the quantification of qualitative measures of economic expectations
spellingShingle A new approach for the quantification of qualitative measures of economic expectations
Clavería González, Óscar
Creixement econòmic
Investigació qualitativa
Anàlisi de regressió
Algorismes genètics
Economic growth
Qualitative research
Regression analysis
Genetic algorithms
title_short A new approach for the quantification of qualitative measures of economic expectations
title_full A new approach for the quantification of qualitative measures of economic expectations
title_fullStr A new approach for the quantification of qualitative measures of economic expectations
title_full_unstemmed A new approach for the quantification of qualitative measures of economic expectations
title_sort A new approach for the quantification of qualitative measures of economic expectations
dc.creator.none.fl_str_mv Clavería González, Óscar
Monte Moreno, Enric
Torra Porras, Salvador
author Clavería González, Óscar
author_facet Clavería González, Óscar
Monte Moreno, Enric
Torra Porras, Salvador
author_role author
author2 Monte Moreno, Enric
Torra Porras, Salvador
author2_role author
author
dc.subject.none.fl_str_mv Creixement econòmic
Investigació qualitativa
Anàlisi de regressió
Algorismes genètics
Economic growth
Qualitative research
Regression analysis
Genetic algorithms
topic Creixement econòmic
Investigació qualitativa
Anàlisi de regressió
Algorismes genètics
Economic growth
Qualitative research
Regression analysis
Genetic algorithms
description In this study a new approach to quantify qualitative survey data about the direction of change is presented. We propose a data-driven procedure based on evolutionary computation that avoids making any assumption about agents' expectations. The research focuses on experts' expectations about the state of the economy from the World Economic Survey in twenty eight countries of the Organisation for Economic Co-operation and Development. The proposed method is used to transform qualitative responses into estimates of economic growth. In a first experiment, we combine agents' expectations about the future to construct a leading indicator of economic activity. In a second experiment, agents' judgements about the present are combined to generate a coincident indicator. Then, we use index tracking to derive the optimal combination of weights for both indicators that best replicates the evolution of economic activity in each country. Finally, we compute several accuracy measures to assess the performance of these estimates in tracking economic growth. The different results across countries have led us to use multidimensional scaling analysis in order to group all economies in four clusters according to their performance.
publishDate 2016
dc.date.none.fl_str_mv 2016
2017
2017
2017
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/2445/117334
url https://hdl.handle.net/2445/117334
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.1007/s11135-016-0416-0
Quality & Quantity, 2016, vol. 51, num. 6, p. 2685-2706
https://doi.org/10.1007/s11135-016-0416-0
dc.rights.none.fl_str_mv (c) Springer Verlag, 2016
info:eu-repo/semantics/openAccess
rights_invalid_str_mv (c) Springer Verlag, 2016
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Verlag
publisher.none.fl_str_mv Springer Verlag
dc.source.none.fl_str_mv Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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