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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/260514
Ver los metadatos del registro completo
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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 |
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reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) |
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CONICET Digital (CONICET) |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.070432 |