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Forecasting with nonlinear time series models

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Author Info

  • Anders Bredahl Kock

    ()
    (CREATES, Aarhus University)

  • Timo Teräsvirta

    ()
    (CREATES, Aarhus University)

Abstract

In this paper, nonlinear models are restricted to mean nonlinear parametric models. Several such models popular in time series econo- metrics are presented and some of their properties discussed. This in- cludes two models based on universal approximators: the Kolmogorov- Gabor polynomial model and two versions of a simple artificial neural network model. Techniques for generating multi-period forecasts from nonlinear models recursively are considered, and the direct (non-recursive) method for this purpose is mentioned as well. Forecasting with com- plex dynamic systems, albeit less frequently applied to economic fore- casting problems, is briefly highlighted. A number of large published studies comparing macroeconomic forecasts obtained using different time series models are discussed, and the paper also contains a small simulation study comparing recursive and direct forecasts in a partic- ular case where the data-generating process is a simple artificial neural network model. Suggestions for further reading conclude the paper.

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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 2010-01.

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Length: 26
Date of creation: 01 Jan 2010
Date of revision:
Handle: RePEc:aah:create:2010-01

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

Related research

Keywords: forecast accuracy; Kolmogorov-Gabor; nearest neigh- bour; neural network; nonlinear regression;

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References

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Citations

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Cited by:
  1. Oscar Claveria & Salvador Torra, 2013. "“Forecasting Business surveys indicators: neural networks vs. time series models”," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics 201320, University of Barcelona, Research Institute of Applied Economics, revised Nov 2013.
  2. Anders Bredahl Kock & Timo Teräsvirta, 2011. "Forecasting performance of three automated modelling techniques during the economic crisis 2007-2009," CREATES Research Papers, School of Economics and Management, University of Aarhus 2011-28, School of Economics and Management, University of Aarhus.
  3. Mehmet Pinar & Thanasis Stengos & M. Ege Yazgan, 2012. "Is there an optimal forecast combination? A stochastic dominance approach applied to the forecast combination puzzle," Working Papers, University of Guelph, Department of Economics and Finance 1206, University of Guelph, Department of Economics and Finance.
  4. Kirstin Hubrich & Timo Teräsvirta, 2013. "Thresholds and Smooth Transitions in Vector Autoregressive Models," CREATES Research Papers, School of Economics and Management, University of Aarhus 2013-18, School of Economics and Management, University of Aarhus.
  5. Souhaib Ben Taieb & Rob J Hyndman, 2014. "Boosting multi-step autoregressive forecasts," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics 13/14, Monash University, Department of Econometrics and Business Statistics.
  6. Anders Bredahl Kock & Timo Teräsvirta, 2011. "Forecasting Macroeconomic Variables using Neural Network Models and Three Automated Model Selection Techniques," CREATES Research Papers, School of Economics and Management, University of Aarhus 2011-27, School of Economics and Management, University of Aarhus.
  7. Jennifer Castle & David Hendry, 2013. "Semi-automatic Non-linear Model selection," Economics Series Working Papers, University of Oxford, Department of Economics 654, University of Oxford, Department of Economics.

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