SSE/EFI Working Paper Series in Economics and Finance
Marcelo C. Medeiros
Building neural network models for time series: A statistical approach
(), Timo Teräsvirta
() and Gianluigi Rech
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 test 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.
Keywords: Model misspecification; neural computing; nonlinear forecasting; nonlinear time series; smooth transition autoregression; sunspot series; threshold autoregression; financial prediction; (follow links to similar papers)
JEL-Codes: C51; C52; C61; G12; (follow links to similar papers)
47 pages, September 1, 2002
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- This paper is published as:
Medeiros, Marcelo C., Timo Teräsvirta and Gianluigi Rech, (2006), 'Building neural network models for time series: A statistical approach', Journal of Forecasting, Vol. 25, pages 49-75
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