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Forecasting with Universal Approximators and a Learning Algorithm

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  • Kock Anders Bredahl

    (Aarhus University and CREATES)

Abstract

This paper applies three universal approximators for forecasting. They are the Artificial Neural Networks, the Kolmogorov-Gabor polynomials, as well as the Elliptic Basis Function Networks. We are particularly interested in the relative performance and stability of these. Even though forecast combination has a long history in econometrics focus has not been on proving loss bounds for the combination rules applied. We apply the Weighted Average Algorithm (WAA) of Kivinen & Warmuth (1999) for which such loss bounds exist. Specifically, one can bound the worst case performance of the WAA compared to the performance of the best single model in the set of models combined from. The use of universal approximators along with a combination scheme for which explicit loss bounds exist should give a solid theoretical foundation to the way the forecasts are performed. The practical performance will be investigated by considering various monthly postwar macroeconomic data sets for the G7 as well as the Scandinavian countries.

Suggested Citation

  • Kock Anders Bredahl, 2011. "Forecasting with Universal Approximators and a Learning Algorithm," Journal of Time Series Econometrics, De Gruyter, vol. 3(3), pages 1-32, October.
    • Handle: RePEc:bpj:jtsmet:v:3:y:2011:i:3:n:3
    DOI: 10.2202/1941-1928.1084
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    References listed on IDEAS

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    1. Engle, Robert F. & White (the late), Halbert (ed.), 1999. "Cointegration, Causality, and Forecasting: Festschrift in Honour of Clive W. J. Granger," OUP Catalogue, Oxford University Press, number 9780198296836.
    2. Anders Bredahl Kock & Timo Teräsvirta, 2016. "Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1753-1779, December.
    3. G. Elliott & C. Granger & A. Timmermann (ed.), 2006. "Handbook of Economic Forecasting," Handbook of Economic Forecasting, Elsevier, edition 1, volume 1, number 1.
    4. Swanson, Norman R & White, Halbert, 1995. "A Model-Selection Approach to Assessing the Information in the Term Structure Using Linear Models and Artificial Neural Networks," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 265-275, July.
    5. White, Halbert, 2006. "Approximate Nonlinear Forecasting Methods," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 9, pages 459-512, Elsevier.
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    Cited by:

    1. Shahid IQBAL & Maqbool H. SIAL, 2016. "Projections of Inflation Dynamics for Pakistan: GMDH Approach," Journal of Economics and Political Economy, KSP Journals, vol. 3(3), pages 536-559, September.
    2. Tae-Hwy Lee & Zhou Xi & Ru Zhang, 2013. "Testing for Neglected Nonlinearity Using Regularized Artificial Neural Networks," Working Papers 201422, University of California at Riverside, Department of Economics, revised Apr 2012.
    3. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, Department of Economics and Business Economics, Aarhus University.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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