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Forecasting with Universal Approximators and a Learning Algorithm

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Author Info

  • Anders Bredahl Kock

    ()
    (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. 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 and 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.

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Bibliographic Info

Paper provided by School of Economics and Management, University of Aarhus in its series CREATES Research Papers with number 2009-18.

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Length: 27
Date of creation: 11 May 2009
Date of revision:
Handle: RePEc:aah:create:2009-18

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Web page: http://www.econ.au.dk/afn/

Related research

Keywords: Forecasting; Universal Approximators; Elliptic Basis Function Network; Forecast Combination; Weighted Average Algorithm;

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Cited by:
  1. Anders Bredahl Kock & Timo Teräsvirta, 2010. "Forecasting with nonlinear time series models," CREATES Research Papers 2010-01, School of Economics and Management, University of Aarhus.

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