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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.

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File URL: http://www.degruyter.com/view/j/snde.2000.4.3/snde.2000.4.3.1059/snde.2000.4.3.1059.xml?format=INT
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Bibliographic Info

Article provided by De Gruyter in its journal Studies in Nonlinear Dynamics & Econometrics.

Volume (Year): 4 (2000)
Issue (Month): 3 (October)
Pages: 1-8

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Handle: RePEc:bpj:sndecm:v:4:y:2000:i:3:n:1

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Web page: http://www.degruyter.com

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Cited by:
  1. 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, 07.
  2. 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.
  3. 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.

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