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dc.contributor.authorRocha, Jordano Vieira
dc.contributor.authorPereira, Pedro L. Valls
dc.date.accessioned2015-07-27T19:21:56Z
dc.date.available2015-07-27T19:21:56Z
dc.date.issued2015-07-27
dc.identifier.siciTD 397
dc.identifier.urihttp://hdl.handle.net/10438/13862
dc.description.abstractThis 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.en_US
dc.language.isoen_US
dc.relation.ispartofseriesEESP - Textos para Discussão;TD 397pt_BR
dc.subjectForecastingpt_BR
dc.subjectNonlinear methodspt_BR
dc.subjectMarkov Switchingpt_BR
dc.subjectSmooth transition autoregressivept_BR
dc.subjectAutometricspt_BR
dc.subjectDummy saturationpt_BR
dc.titleForecast comparison with nonlinear methods for Brazilian industrial productionen_US
dc.typeWorking Paperen_US
dc.subject.areaEconomiapt_BR
dc.contributor.unidadefgvEscolas::EESPpt_BR
dc.subject.bibliodataPrevisão econômicapt_BR
dc.subject.bibliodataEconometriapt_BR


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