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Nonlinear Correlograms and Partial Autocorrelograms

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Author Info
Heather M. Anderson ()
Farshid Vahid ()

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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/depts/ebs/pubs/wpapers/2003/wp19-03.pdf
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Publisher 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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Find related papers by JEL classification:
C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models
C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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References listed on IDEAS
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  3. 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. [Downloadable!] (restricted)
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  5. repec:att:wimass:199520 is not listed on IDEAS
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  7. Bierens, Herman J, 1990. "A Consistent Conditional Moment Test of Functional Form," Econometrica, Econometric Society, vol. 58(6), pages 1443-58, November. [Downloadable!] (restricted)
  8. 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. [Downloadable!] (restricted)
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  11. C. W. Granger & E. Maasoumi & J. Racine, 2004. "A Dependence Metric for Possibly Nonlinear Processes," Journal of Time Series Analysis, Blackwell Publishing, vol. 25(5), pages 649-669, 09. [Downloadable!] (restricted)
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