Submitted by Jordano Vieira Rocha (jordanorocha@hotmail.com) on 2015-04-30T08:48:24Z No. of bitstreams: 1 Dissertação - Jordano Vieira Rocha.pdf: 1057882 bytes, checksum: 1ba84113f5ec0c31d9c99f3bebe4714d (MD5) / Approved for entry into archive by Suzinei Teles Garcia Garcia (suzinei.garcia@fgv.br) on 2015-04-30T13:02:56Z (GMT) No. of bitstreams: 1 Dissertação - Jordano Vieira Rocha.pdf: 1057882 bytes, checksum: 1ba84113f5ec0c31d9c99f3bebe4714d (MD5) / Made available in DSpace on 2015-04-30T17:23:54Z (GMT). No. of bitstreams: 1 Dissertação - Jordano Vieira Rocha.pdf: 1057882 bytes, checksum: 1ba84113f5ec0c31d9c99f3bebe4714d (MD5) Previous issue date: 2015-04-07 / This work assesses the forecasts of three nonlinear methods — Markov Switching Autoregressive Model, Logistic Smooth Transition Autoregressive Model, and Autometrics 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 differencing device. The results show that the step dummy saturation and the logistic smooth transition autoregressive can be superior to the double differencing device, but the linear autoregressive model is more accurate than all the other methods analyzed. / Este trabalho avalia as previsões de três métodos não lineares — Markov Switching Autoregressive Model, Logistic Smooth Transition Autoregressive Model e Autometrics com Dummy Saturation — para a produção industrial mensal brasileira e testa se elas são mais precisas que aquelas de preditores naive, como o modelo autorregressivo de ordem p e o mecanismo de double differencing. Os resultados mostram que a saturação com dummies de degrau e o Logistic Smooth Transition Autoregressive Model podem ser superiores ao mecanismo de double differencing, mas o modelo linear autoregressivo é mais preciso que todos os outros métodos analisados.