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Testing for Neglected Nonlinearity in Cointegrating Relationships

Author

Listed:
  • Andrew P. Blake

    (Bank of England)

  • George Kapetanios

    (Queen Mary, University of London)

Abstract

This paper proposes pure significance tests for the absence of nonlinearity in cointegrating relationships. No assumption of the functional form of the nonlinearity is made. It is envisaged that the application of such tests could form the first step towards specifying a nonlinear cointegrating relationship for empirical modelling. The asymptotic and small sample properties of our tests are investigated, where special attention is paid to the role of nuisance parameters and a potential resolution using the bootstrap.

Suggested Citation

  • Andrew P. Blake & George Kapetanios, 2004. "Testing for Neglected Nonlinearity in Cointegrating Relationships," Working Papers 508, Queen Mary University of London, School of Economics and Finance.
    • Handle: RePEc:qmw:qmwecw:508
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    File URL: https://www.qmul.ac.uk/sef/media/econ/research/workingpapers/2004/items/wp508.pdf
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    References listed on IDEAS

    as
    1. Yoosoon Chang & Joon Y. Park & Peter C. B. Phillips, 2001. "Nonlinear econometric models with cointegrated and deterministically trending regressors," Econometrics Journal, Royal Economic Society, vol. 4(1), pages 1-36.
    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.
    3. Ibragimov, Rustam & Phillips, Peter C.B., 2008. "Regression Asymptotics Using Martingale Convergence Methods," Econometric Theory, Cambridge University Press, vol. 24(4), pages 888-947, August.
    4. 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.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Kanazawa, Nobuyuki, 2020. "Radial basis functions neural networks for nonlinear time series analysis and time-varying effects of supply shocks," Journal of Macroeconomics, Elsevier, vol. 64(C).
    2. Lee Jinu, 2019. "A Neural Network Method for Nonlinear Time Series Analysis," Journal of Time Series Econometrics, De Gruyter, vol. 11(1), pages 1-18, January.

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    More about this item

    Keywords

    Cointegration; Nonlinearity; Neural networks; Bootstrap;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics

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