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Forecasting inflation with ANN models

dc.creatorRíos Ibáñez, Vicente
dc.date.accessioned2020-12-23T14:12:52Z
dc.date.available2020-12-23T14:12:52Z
dc.date.created2010-12-01
dc.date.issued2010-12-01
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dc.identifier.urihttp://repositorio.bcp.gov.py/handle/123456789/120
dc.description.abstractIn this research I investigate the alternative methodology of training artificial neural networks models with the early stopping procedure and I analyze their outcomes in terms of accuracy when forecasting monthly Paraguayan inflation time series. The results show that despite of neural network modelling being a competitive alternative to classical linear modelling it doesn‟t improve the overall forecast performance of best ARMA specifications selected through common in-sample estimation procedures, in a set of four control subsamples of 24 months each, ranging from 2002:04 to 2010:04. However, it is also a remarkable feature of all the checks performed in this research, that artificial neural network models outperform ARMA specifications in 24-steps-ahead horizon forecasts in all the subsamples of control.
dc.format.extent41 páginas
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherBCP
dc.relation.ispartofDocumentos de Trabajo
dc.relation.ispartofseriesDocumento de Trabajo
dc.relation.isversionofDocumento de Trabajo; N° 12
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/
dc.subjectINFLACIÓN
dc.titleForecasting inflation with ANN models
dc.title.alternativePronóstico de la inflación con modelos ANN
dc.typeWorking Paper
dc.subject.jelE00
dc.subject.jelspaE00
dc.subject.keywordINFLATION
dc.rights.accessRightsOpen Access
dc.type.spaDocumento de Trabajo
dc.type.hasversionPublished Version
dc.rights.ccCC0 1.0 Universal
dc.rights.spaAcceso abierto


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http://creativecommons.org/publicdomain/zero/1.0/This work is licensed under a Creative Commons Reconocimiento-NoComercial 4.0.This document has been deposited by the author (s) under the following certificate of deposit