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Building Neural Network Models for Time Series: A Statistical Approach

Marcelo Medeiros (), Timo Teräsvirta () and Gianluigi Rech
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Gianluigi Rech: Quantitative Analysis, Electrabel, Louvain-la-Neuve, Belgium

No 461, Textos para discussão from Department of Economics PUC-Rio (Brazil)

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-codes: C22 C51 C52 C61 G12 (search for similar items in EconPapers)
Date: 2002-08
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Forthcoming in the Journal of Forecasting

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Journal Article: Building neural network models for time series: a statistical approach (2006) Downloads
Working Paper: Building neural network models for time series: A statistical approach (2002) Downloads
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