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On a robust test for SETAR-type nonlinearity in time series analysis

  • King Chi Hung

    (Chinese University of Hong Kong, Hong Kong)

  • Siu Hung Cheung
  • Wai-Sum Chan

    (Chinese University of Hong Kong, Hong Kong)

  • Li-Xin Zhang

    (Zhejiang University, PR China)

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    There has been growing interest in exploiting potential forecast gains from the nonlinear structure of self-exciting threshold autoregressive (SETAR) models. Statistical tests have been proposed in the literature to help analysts check for the presence of SETAR-type nonlinearities in observed time series. However, previous studies show that classical nonlinearity tests are not robust to additive outliers. In practice, time series outliers are not uncommonly encountered. It is important to develop a more robust test for SETAR-type nonlinearity in time series analysis and forecasting. In this paper we propose a new robust nonlinearity test and the asymptotic null distribution of the test statistic is derived. A Monte Carlo experiment is carried out to compare the power of the proposed test with other existing tests under the influence of time series outliers. The effects of additive outliers on nonlinearity tests with misspecification of the autoregressive order are also studied. The results indicate that the proposed method is preferable to the classical tests when the observations are contaminated with outliers. Finally, we provide illustrative examples by applying the statistical tests to three real datasets. Copyright © 2009 John Wiley & Sons, Ltd.

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    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 28 (2009)
    Issue (Month): 5 ()
    Pages: 445-464

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    Handle: RePEc:jof:jforec:v:28:y:2009:i:5:p:445-464
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