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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.eng
dc.language.isoeng
dc.relation.ispartofseriesEESP - Textos para Discussão;TD 397por
dc.subjectForecastingpor
dc.subjectNonlinear methodspor
dc.subjectMarkov Switchingpor
dc.subjectSmooth transition autoregressivepor
dc.subjectAutometricspor
dc.subjectDummy saturationpor
dc.titleForecast comparison with nonlinear methods for Brazilian industrial productioneng
dc.typeWorking Papereng
dc.subject.areaEconomiapor
dc.contributor.unidadefgvEscolas::EESPpor
dc.subject.bibliodataPrevisão econômicapor
dc.subject.bibliodataEconometriapor


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