Brasil | Working Paper
dc.contributorEscolas::EESP
dc.creatorRocha, Jordano Vieira
dc.creatorPereira, Pedro L. Valls
dc.date.accessioned2015-07-27T19:21:56Z
dc.date.accessioned2022-11-03T20:24:36Z
dc.date.available2015-07-27T19:21:56Z
dc.date.available2022-11-03T20:24:36Z
dc.date.created2015-07-27T19:21:56Z
dc.date.issued2015-07-27
dc.identifierTD 397
dc.identifierhttp://hdl.handle.net/10438/13862
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5037825
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.
dc.languageeng
dc.relationEESP - Textos para Discussão;TD 397
dc.subjectForecasting
dc.subjectNon-linear methods
dc.subjectMarkov switching
dc.subjectSmooth transition autoregressive
dc.subjectAutometrics
dc.subjectDummy saturation
dc.titleForecast comparison with nonlinear methods for Brazilian industrial production
dc.typeWorking Paper


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