Modelling exchange rates: Smooth transitions, neural networks, and linear models - Marcelo Cunha Medeiros; Álvaro Veiga; Carlos Eduardo Pedreira (Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia, 2000)
The goal of this paper is to test for and model nonlinearities in several monthly exchange rates time series. We apply two different nonlinear alternatives, namely: the artificial neural network time series model estimated with Bayesian regularization and a flexible smooth transition specifica-tion, called the neuro-coefficient smooth transition autoregression. The linearity test rejects the null hypothesis of linearity in ten out of fourteen series. We compare, using different measures, the fore-casting performance of the nonlinear specifications with the linear autoregression and the random walk models.
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