Forecast comparison with nonlinear methods for Brazilian industrial production
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional do FGV (FGV Repositório Digital) |
Texto Completo: | http://hdl.handle.net/10438/13862 |
Resumo: | This work assesses the forecasts of three nonlinear methods | Markov Switching Autoregressive Model, Logistic Smooth Transition Auto-regressive Model, and Auto-metrics with Dummy Saturation | for the Brazilian monthly industrial production and tests if they are more accurate than those of naive predictors such as the autoregressive model of order p and the double di erencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double di erencing device, but the linear autoregressive model is more accurate than all the other methods analyzed. |
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Rocha, Jordano VieiraPereira, Pedro L. VallsEscolas::EESP2015-07-27T19:21:56Z2015-07-27T19:21:56Z2015-07-27TD 397http://hdl.handle.net/10438/13862This work assesses the forecasts of three nonlinear methods | Markov Switching Autoregressive Model, Logistic Smooth Transition Auto-regressive Model, and Auto-metrics with Dummy Saturation | for the Brazilian monthly industrial production and tests if they are more accurate than those of naive predictors such as the autoregressive model of order p and the double di erencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double di erencing device, but the linear autoregressive model is more accurate than all the other methods analyzed.engEESP - Textos para Discussão;TD 397ForecastingNon-linear methodsMarkov switchingSmooth transition autoregressiveAutometricsDummy saturationEconomiaPrevisão econômicaEconometriaForecast comparison with nonlinear methods for Brazilian industrial productioninfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlereponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessORIGINALTD 397 - CEQEF 24 - Jordano Vieira Rocha e Pedro L. Valls Pereira.pdfTD 397 - CEQEF 24 - Jordano Vieira Rocha e Pedro L. 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dc.title.eng.fl_str_mv |
Forecast comparison with nonlinear methods for Brazilian industrial production |
title |
Forecast comparison with nonlinear methods for Brazilian industrial production |
spellingShingle |
Forecast comparison with nonlinear methods for Brazilian industrial production Rocha, Jordano Vieira Forecasting Non-linear methods Markov switching Smooth transition autoregressive Autometrics Dummy saturation Economia Previsão econômica Econometria |
title_short |
Forecast comparison with nonlinear methods for Brazilian industrial production |
title_full |
Forecast comparison with nonlinear methods for Brazilian industrial production |
title_fullStr |
Forecast comparison with nonlinear methods for Brazilian industrial production |
title_full_unstemmed |
Forecast comparison with nonlinear methods for Brazilian industrial production |
title_sort |
Forecast comparison with nonlinear methods for Brazilian industrial production |
author |
Rocha, Jordano Vieira |
author_facet |
Rocha, Jordano Vieira Pereira, Pedro L. Valls |
author_role |
author |
author2 |
Pereira, Pedro L. Valls |
author2_role |
author |
dc.contributor.unidadefgv.por.fl_str_mv |
Escolas::EESP |
dc.contributor.author.fl_str_mv |
Rocha, Jordano Vieira Pereira, Pedro L. Valls |
dc.subject.por.fl_str_mv |
Forecasting |
topic |
Forecasting Non-linear methods Markov switching Smooth transition autoregressive Autometrics Dummy saturation Economia Previsão econômica Econometria |
dc.subject.eng.fl_str_mv |
Non-linear methods Markov switching Smooth transition autoregressive Autometrics Dummy saturation |
dc.subject.area.por.fl_str_mv |
Economia |
dc.subject.bibliodata.por.fl_str_mv |
Previsão econômica Econometria |
description |
This work assesses the forecasts of three nonlinear methods | Markov Switching Autoregressive Model, Logistic Smooth Transition Auto-regressive Model, and Auto-metrics with Dummy Saturation | for the Brazilian monthly industrial production and tests if they are more accurate than those of naive predictors such as the autoregressive model of order p and the double di erencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double di erencing device, but the linear autoregressive model is more accurate than all the other methods analyzed. |
publishDate |
2015 |
dc.date.accessioned.fl_str_mv |
2015-07-27T19:21:56Z |
dc.date.available.fl_str_mv |
2015-07-27T19:21:56Z |
dc.date.issued.fl_str_mv |
2015-07-27 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
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dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10438/13862 |
dc.identifier.sici.none.fl_str_mv |
TD 397 |
identifier_str_mv |
TD 397 |
url |
http://hdl.handle.net/10438/13862 |
dc.language.iso.fl_str_mv |
eng |
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eng |
dc.relation.ispartofseries.por.fl_str_mv |
EESP - Textos para Discussão;TD 397 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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