Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/175948
Authors: 
Medeiros, Marcelo C.
Terasvirta, Timo
Rech, Gianluigi
Year of Publication: 
2002
Series/Report no.: 
Texto para discussão 461
Abstract: 
This paper is concerned with modelling time series by single hidden layer feedforward neural network models. A coherent modelling strategy based on statistical inference is presented. Variable selection is carried out using existing techniques. The problem of selecting the number of hidden units is solved by sequentially applying Lagrange multiplier type tests, with the aim of avoiding the estimation of unidentified models. Misspecification tests are derived for evaluating an estimated neural network model. A small-sample simulation experiment is carried out to show how the proposed modelling strategy works and how the misspecification tests behave in small samples. Two applications to real time series, one univariate and the other multivariate, are considered as well. Sets of one-step-ahead forecasts are constructed and forecast accuracy is compared with that of other nonlinear models applied to the same series.
JEL: 
C22
C51
C52
C61
G12
Document Type: 
Working Paper
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