Forecast comparison with nonlinear methods for Brazilian industrial production
Carregando...
Arquivos
Data
2015-04-07
Autores
Orientador(res)
Pereira, Pedro L. Valls
Métricas
Título da Revista
ISSN da Revista
Título de Volume
Resumo
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.