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dc.creatorOspina, Javier H.
dc.creatorPadilla Ospina, Ana Milena
dc.date.accessioned2020-05-19T22:28:57Z
dc.date.available2020-05-19T22:28:57Z
dc.date.issued2019-11-15
dc.identifier.citationOspina-Holguín, J., & Ospina-Holguín, A. (2019). Penalised regressions vs. autoregressive moving average models for forecasting inflation. ECONÓMICAS CUC, 41(1), 65-80. https://doi.org/10.17981/econcuc.41.1.2020.Econ.3spa
dc.identifier.issn2382-3860
dc.identifier.issn0120-3932
dc.identifier.urihttps://hdl.handle.net/11323/6279
dc.description.abstractThis article relates the Seasonal Autoregressive Moving Average Models (SARMA) to linear regression. Based on this relationship, the paper shows that penalized linear models can outperform the out-of-sample forecast accuracy of the best SARMA models in forecasting inflation as a function of past values, due to penalization and cross-validation. The paper constructs a minimal functional example using edge regression to compare both competing approaches to forecasting monthly inflation in 35 selected countries of the Organization for Economic Cooperation and Development and in three groups of coun-tries. The results empirically test the hypothesis that penalized linear regression, and edge regression in particular, can outperform the best standard SARMA models calculated through a grid search when fore-casting inflation. Thus, a new and effective technique for forecasting inflation based on past values is provided for use by financial analysts and investors. The results indicate that more attention should be paid to automatic learning techniques for forecasting inflation time series, even as basic as penalized linear regressions, because of their superior empirical performance.spa
dc.description.abstractEste artículo relaciona los Modelos Autorregresivos Estacionales de Media Móvil (SARMA) con la regresión lineal. Sobre la base de esta relación, el documento muestra que los modelos lineales penalizados pueden superar la precisión del pronóstico fuera de la muestra de los mejores modelos SARMA al pronosticar la inflación en función de valo-res pasados, debido a la penalización y a la validación cruzada. El artí-culo construye un ejemplo funcional mínimo utilizando la regresión de arista para comparar ambos enfoques que compiten al pronosticar la inflación mensual en 35 países seleccionados de la Organización para la Cooperación y el Desarrollo Económico y en tres grupos de países. Los resultados verifican empíricamente la hipótesis de que la regre-sión lineal penalizada, y la regresión de arista en particular, puede superar a los mejores modelos estándar SARMA calculados a través de una búsqueda de cuadrícula cuando se pronostica la inflación. Así, se proporciona una técnica nueva y efectiva para pronosticar la infla-ción basada en valores pasados para el uso de analistas financieros e inversores. Los resultados indican que se debe prestar más atención a las técnicas de aprendizaje automático para el pronóstico de series de tiempo de la inflación, incluso tan básicas como las regresiones linea-les penalizadas, debido a su rendimiento empírico superior.spa
dc.language.isoengspa
dc.publisherCorporación Universidad de la Costa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.sourceECONÓMICAS CUCspa
dc.subjectRidge regressionspa
dc.subjectPenalised linear modelspa
dc.subjectARMAspa
dc.subjectSARMAspa
dc.subjectInflation forecastingspa
dc.subjectRegresión de aristaspa
dc.subjectModelo lineal penalizadospa
dc.subjectPronóstico de la inflaciónspa
dc.titlePenalised regressions vs. autoregressive moving average models for forecasting inflationspa
dc.title.alternativeRegresiones penalizadas vs. modelos autorregresivos de media móvil para pronosticar la inflaciónspa
dc.typeArticlespa
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dc.source.urlhttps://revistascientificas.cuc.edu.co/economicascuc/article/view/2657/2824spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.doihttps://doi.org/10.17981/econcuc.41.1.2020.Econ.3


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