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p-Value Adjustments for Multiple Tests for Nonlinearity

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  • Psaradakis Zacharias

    (Birkbeck College, University of London)

Abstract

When the hypothesis of linearity of a univariate time series model is tested using a battery of tests for neglected nonlinearity, the probability of one or more tests' leading to a false rejection increases with the number of tests being performed. This paper discusses how this undesirable effect of multiple testing may be controlled by means of some simple and easily implemented procedures. Monte Carlo experiments are used to demonstrate the finite-sample effectiveness of the various methods, and an analysis of the nonlinearity properties of GDP data from five OECD countries is presented as an illustration.

Suggested Citation

  • Psaradakis Zacharias, 2000. "p-Value Adjustments for Multiple Tests for Nonlinearity," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 4(3), pages 1-8, October.
    • Handle: RePEc:bpj:sndecm:v:4:y:2000:i:3:n:1
    DOI: 10.2202/1558-3708.1059
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    References listed on IDEAS

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    1. William A. Barnett & A. Ronald Gallant & Melvin J. Hinich & Jochen A. Jungeilges & Daniel T. Kaplan, 2004. "A Single-Blind Controlled Competition Among Tests for Nonlinearity and Chaos," Contributions to Economic Analysis, in: Functional Structure and Approximation in Econometrics, pages 581-615, Emerald Group Publishing Limited.
    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. Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207.
    4. Clive Granger & Tae-Hwy Lee, 1999. "The effect of aggregation on nonlinearity," Econometric Reviews, Taylor & Francis Journals, vol. 18(3), pages 259-269.
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    Citations

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    Cited by:

    1. Zacharias Psaradakis & Marián Vávra, 2019. "Portmanteau tests for linearity of stationary time series," Econometric Reviews, Taylor & Francis Journals, vol. 38(2), pages 248-262, February.
    2. Aaron D. Smallwood, 2016. "A Monte Carlo Investigation of Unit Root Tests and Long Memory in Detecting Mean Reversion in I(0) Regime Switching, Structural Break, and Nonlinear Data," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 986-1012, June.
    3. Jorge Belaire-Franch & Kwaku Opong, 2013. "A Time Series Analysis of U.K. Construction and Real Estate Indices," The Journal of Real Estate Finance and Economics, Springer, vol. 46(3), pages 516-542, April.
    4. Jorge Belaire-Franch & Dulce Contreras, 2004. "A power comparison among tests for time reversibility," Economics Bulletin, AccessEcon, vol. 3(23), pages 1-17.
    5. Steven Cook & Alan Speight, 2006. "International Business Cycle Asymmetry and Time Irreversible Nonlinearities," Journal of Applied Statistics, Taylor & Francis Journals, vol. 33(10), pages 1051-1065.
    6. Amélie Charles & Olivier Darné, 2009. "Variance‐Ratio Tests Of Random Walk: An Overview," Journal of Economic Surveys, Wiley Blackwell, vol. 23(3), pages 503-527, July.
    7. Dilip Kumar & Srinivasan Maheswaran, 2014. "Are major global stock markets efficient? An application of the martingale difference hypothesis with wild bootstrap," American Journal of Finance and Accounting, Inderscience Enterprises Ltd, vol. 3(2/3/4), pages 217-233.
    8. Belaire-Franch, Jorge & Opong, Kwaku K., 2005. "Some evidence of random walk behavior of Euro exchange rates using ranks and signs," Journal of Banking & Finance, Elsevier, vol. 29(7), pages 1631-1643, July.
    9. Steven Cook & Alan Speight, 2006. "Time deformation in UK consumers' expenditure: an empirical analysis of highly disaggregated data," Applied Economics Letters, Taylor & Francis Journals, vol. 13(8), pages 471-478.
    10. Steven Cook & Alan Speight, 2005. "A deeper look at asymmetries in UK consumers' expenditure: the nonparametric analysis of 100 disaggregates," Applied Economics, Taylor & Francis Journals, vol. 37(8), pages 893-900.

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