How much is too much inflation?: Classifying inflationary regimes

Autores
De Mier, Manuel; Delbianco, Fernando Andrés
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Purpose – Existing classifications of inflationary regimes often rely on subjective judgments, hindering objectivity and accuracy. This study proposes a novel, data-driven approach to address this limitation. Design/methodology/approach – We combine unsupervised clustering and classification tree methods to analyze Argentine inflation data from 1943 to 2022. Two smoothing techniques are introduced: a measure of temporal contiguity and a rolling majority rule method. The resulting regimes are compared to existing classifications based on their explanatory power for inflation-relative price variability. Findings – Ourmethod identifies distinctinflationary regimes, demonstrating significantimprovementin objectivity and accuracy compared to existing literature. The regimes capture key historical periods and exhibit a strong association with inflation-relative price variability, providing valuable insights into Argentine inflation dynamics. Originality/value – This study offers a novel methodological framework for constructing objective and accurate inflationary regimes, free from subjective biases. This approach holds potential for application to other contexts and contributes to a more nuanced understanding of inflation dynamics.
Fil: De Mier, Manuel. Universidad Torcuato Di Tella; Argentina
Fil: Delbianco, Fernando Andrés. Universidad Nacional del Sur. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina
Materia
Inflation
Periodization
Clustering
Classification trees
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/260514

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network_name_str CONICET Digital (CONICET)
spelling How much is too much inflation?: Classifying inflationary regimesDe Mier, ManuelDelbianco, Fernando AndrésInflationPeriodizationClusteringClassification treeshttps://purl.org/becyt/ford/5.2https://purl.org/becyt/ford/5Purpose – Existing classifications of inflationary regimes often rely on subjective judgments, hindering objectivity and accuracy. This study proposes a novel, data-driven approach to address this limitation. Design/methodology/approach – We combine unsupervised clustering and classification tree methods to analyze Argentine inflation data from 1943 to 2022. Two smoothing techniques are introduced: a measure of temporal contiguity and a rolling majority rule method. The resulting regimes are compared to existing classifications based on their explanatory power for inflation-relative price variability. Findings – Ourmethod identifies distinctinflationary regimes, demonstrating significantimprovementin objectivity and accuracy compared to existing literature. The regimes capture key historical periods and exhibit a strong association with inflation-relative price variability, providing valuable insights into Argentine inflation dynamics. Originality/value – This study offers a novel methodological framework for constructing objective and accurate inflationary regimes, free from subjective biases. This approach holds potential for application to other contexts and contributes to a more nuanced understanding of inflation dynamics.Fil: De Mier, Manuel. Universidad Torcuato Di Tella; ArgentinaFil: Delbianco, Fernando Andrés. Universidad Nacional del Sur. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; ArgentinaEmerald Publishing2025-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/260514De Mier, Manuel; Delbianco, Fernando Andrés; How much is too much inflation?: Classifying inflationary regimes; Emerald Publishing; EconomiA; 2-2025; 1-231517-75802358-2820CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1108/ECON-02-2024-0025info:eu-repo/semantics/altIdentifier/url/https://www.emerald.com/insight/content/doi/10.1108/econ-02-2024-0025/full/htmlinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T10:23:48Zoai:ri.conicet.gov.ar:11336/260514instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 10:23:48.443CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv How much is too much inflation?: Classifying inflationary regimes
title How much is too much inflation?: Classifying inflationary regimes
spellingShingle How much is too much inflation?: Classifying inflationary regimes
De Mier, Manuel
Inflation
Periodization
Clustering
Classification trees
title_short How much is too much inflation?: Classifying inflationary regimes
title_full How much is too much inflation?: Classifying inflationary regimes
title_fullStr How much is too much inflation?: Classifying inflationary regimes
title_full_unstemmed How much is too much inflation?: Classifying inflationary regimes
title_sort How much is too much inflation?: Classifying inflationary regimes
dc.creator.none.fl_str_mv De Mier, Manuel
Delbianco, Fernando Andrés
author De Mier, Manuel
author_facet De Mier, Manuel
Delbianco, Fernando Andrés
author_role author
author2 Delbianco, Fernando Andrés
author2_role author
dc.subject.none.fl_str_mv Inflation
Periodization
Clustering
Classification trees
topic Inflation
Periodization
Clustering
Classification trees
purl_subject.fl_str_mv https://purl.org/becyt/ford/5.2
https://purl.org/becyt/ford/5
dc.description.none.fl_txt_mv Purpose – Existing classifications of inflationary regimes often rely on subjective judgments, hindering objectivity and accuracy. This study proposes a novel, data-driven approach to address this limitation. Design/methodology/approach – We combine unsupervised clustering and classification tree methods to analyze Argentine inflation data from 1943 to 2022. Two smoothing techniques are introduced: a measure of temporal contiguity and a rolling majority rule method. The resulting regimes are compared to existing classifications based on their explanatory power for inflation-relative price variability. Findings – Ourmethod identifies distinctinflationary regimes, demonstrating significantimprovementin objectivity and accuracy compared to existing literature. The regimes capture key historical periods and exhibit a strong association with inflation-relative price variability, providing valuable insights into Argentine inflation dynamics. Originality/value – This study offers a novel methodological framework for constructing objective and accurate inflationary regimes, free from subjective biases. This approach holds potential for application to other contexts and contributes to a more nuanced understanding of inflation dynamics.
Fil: De Mier, Manuel. Universidad Torcuato Di Tella; Argentina
Fil: Delbianco, Fernando Andrés. Universidad Nacional del Sur. Departamento de Economía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina
description Purpose – Existing classifications of inflationary regimes often rely on subjective judgments, hindering objectivity and accuracy. This study proposes a novel, data-driven approach to address this limitation. Design/methodology/approach – We combine unsupervised clustering and classification tree methods to analyze Argentine inflation data from 1943 to 2022. Two smoothing techniques are introduced: a measure of temporal contiguity and a rolling majority rule method. The resulting regimes are compared to existing classifications based on their explanatory power for inflation-relative price variability. Findings – Ourmethod identifies distinctinflationary regimes, demonstrating significantimprovementin objectivity and accuracy compared to existing literature. The regimes capture key historical periods and exhibit a strong association with inflation-relative price variability, providing valuable insights into Argentine inflation dynamics. Originality/value – This study offers a novel methodological framework for constructing objective and accurate inflationary regimes, free from subjective biases. This approach holds potential for application to other contexts and contributes to a more nuanced understanding of inflation dynamics.
publishDate 2025
dc.date.none.fl_str_mv 2025-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/260514
De Mier, Manuel; Delbianco, Fernando Andrés; How much is too much inflation?: Classifying inflationary regimes; Emerald Publishing; EconomiA; 2-2025; 1-23
1517-7580
2358-2820
CONICET Digital
CONICET
url http://hdl.handle.net/11336/260514
identifier_str_mv De Mier, Manuel; Delbianco, Fernando Andrés; How much is too much inflation?: Classifying inflationary regimes; Emerald Publishing; EconomiA; 2-2025; 1-23
1517-7580
2358-2820
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1108/ECON-02-2024-0025
info:eu-repo/semantics/altIdentifier/url/https://www.emerald.com/insight/content/doi/10.1108/econ-02-2024-0025/full/html
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Emerald Publishing
publisher.none.fl_str_mv Emerald Publishing
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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