Scandinavian Working Papers in Economics

SSE/EFI Working Paper Series in Economics and Finance,
Stockholm School of Economics

No 508: Building neural network models for time series: A statistical approach

Marcelo C. Medeiros (), Timo Teräsvirta () and Gianluigi Rech
Additional contact information
Marcelo C. Medeiros: Department of Economics, Pontifical Catholic University of Rio de Janeiro, Postal: Rua Marquês de São Vicente, 225 - Gávea, 22453-900 Rio de Janeiro, RJ, Brazil
Timo Teräsvirta: Dept. of Economic Statistics, Stockholm School of Economics, Postal: Stockholm School of Economics, P.O. Box 6501, SE-113 83 Stockholm, Sweden
Gianluigi Rech: Quantitative Analysis, Electrabel, Postal: B-1348 Louvain-la-Neuve, Belgium

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

JEL-codes: C51; C52; C61; G12

47 pages, September 1, 2002

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Published as
Marcelo C. Medeiros, 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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