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Building neural network models for time series: A statistical approach

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Author Info

  • Medeiros, Marcelo C.

    ()
    (Department of Economics, Pontifical Catholic University of Rio de Janeiro)

  • Teräsvirta, Timo

    ()
    (Dept. of Economic Statistics, Stockholm School of Economics)

  • Rech, Gianluigi

    (Quantitative Analysis, Electrabel)

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.

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File URL: http://swopec.hhs.se/hastef/papers/hastef0508.ex.zip
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Bibliographic Info

Paper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 508.

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Length: 47 pages
Date of creation: 01 Sep 2002
Date of revision:
Publication status: Published in Journal of Forecasting, 2006, pages 49-75.
Handle: RePEc:hhs:hastef:0508

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Related research

Keywords: Model misspecification; neural computing; nonlinear forecasting; nonlinear time series; smooth transition autoregression; sunspot series; threshold autoregression; financial prediction;

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