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Nonlinear Correlograms and Partial Autocorrelograms

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  • Heather M. Anderson

    ()

  • Farshid Vahid

    ()

Abstract

This paper proposes neural network based measures of predictability in conditional mean, and then uses them to construct nonlinear analogues to autocorrelograms and partial autocorrelograms. In contrast to other measures of nonlinear dependence that rely on nonparametric estimation of densities or multivariate integration, our autocorrelograms are simple to calculate and appear to work well in relatively small samples.

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File URL: http://www.buseco.monash.edu.au/ebs/pubs/wpapers/2003/wp19-03.pdf
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Bibliographic Info

Paper provided by Monash University, Department of Econometrics and Business Statistics in its series Monash Econometrics and Business Statistics Working Papers with number 19/03.

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Length: 29 pages
Date of creation: Nov 2003
Date of revision:
Handle: RePEc:msh:ebswps:2003-19

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Related research

Keywords: Nonlinear autocorrelograms; Nonlinear time series models; Neural networks; Model selection criteria; Nonlinear partial autocorrelograms;

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References

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  1. Terasvirta, T & Anderson, H M, 1992. "Characterizing Nonlinearities in Business Cycles Using Smooth Transition Autoregressive Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 7(S), pages S119-36, Suppl. De.
  2. Lee, Tae-Hwy & White, Halbert & Granger, Clive W. J., 1993. "Testing for neglected nonlinearity in time series models : A comparison of neural network methods and alternative tests," Journal of Econometrics, Elsevier, vol. 56(3), pages 269-290, April.
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Cited by:
  1. Yoon, Gawon, 2010. "Do real exchange rates really follow threshold autoregressive or exponential smooth transition autoregressive models?," Economic Modelling, Elsevier, vol. 27(2), pages 605-612, March.

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