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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, Postal: B-1348 Louvain-la-Neuve, Belgium

No 508, SSE/EFI Working Paper Series in Economics and Finance from Stockholm School of Economics

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 (search for similar items in EconPapers)
JEL-codes: C51 C52 C61 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-cmp, nep-ecm and nep-ets
Date: 2002-09-01
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Published in Journal of Forecasting, 2006, pages 49-75.

Downloads: (external link) Matlab code for paper (application/zip)

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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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