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Bootstrap Neural Network Cointegration Tests Against Nonlinear Alternative Hypotheses

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  • Kapetanios George

    (Queen Mary University of London)

Abstract

This paper introduces bootstrap neural network pure significance tests for the no cointegration hypothesis against nonlinear cointegration alternatives. The theoretical properties of the tests are discussed and a Monte Carlo investigation of their small sample properties is undertaken.

Suggested Citation

  • Kapetanios George, 2003. "Bootstrap Neural Network Cointegration Tests Against Nonlinear Alternative Hypotheses," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 7(2), pages 1-16, July.
    • Handle: RePEc:bpj:sndecm:v:7:y:2003:i:2:n:2
    DOI: 10.2202/1558-3708.1099
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    References listed on IDEAS

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    1. Sin, Chor-Yiu & White, Halbert, 1996. "Information criteria for selecting possibly misspecified parametric models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 207-225.
    2. Park, Joon Y. & Phillips, Peter C.B., 1999. "Asymptotics For Nonlinear Transformations Of Integrated Time Series," Econometric Theory, Cambridge University Press, vol. 15(3), pages 269-298, June.
    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.
    4. George Kapetanios, 2001. "Model Selection in Threshold Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(6), pages 733-754, November.
    5. Li, Hongyi & Maddala, G. S., 1997. "Bootstrapping cointegrating regressions," Journal of Econometrics, Elsevier, vol. 80(2), pages 297-318, October.
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    Cited by:

    1. Hong, Seung Hyun & Phillips, Peter C. B., 2010. "Testing Linearity in Cointegrating Relations With an Application to Purchasing Power Parity," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 96-114.

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