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On Tests for Self‐Exciting Threshold Autoregressive‐Type Non‐Linearity in Partially Observed Time Series

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  • Howell Tong
  • Iris Yeung

Abstract

We have adapted and extended the Petruccelli–Davies test, Petruccelli's test and Tsay's test for non‐linearity in time series to cope with partially observed series. The Kalman filtering algorithm is used in the estimation stage to realize the adaptation. Two of the adapted tests are checked with a Monte Carlo study and all three tests are applied to three real series from the financial world. The fine tuning achieved by allowing for closing date effects offers further insights.

Suggested Citation

  • Howell Tong & Iris Yeung, 1991. "On Tests for Self‐Exciting Threshold Autoregressive‐Type Non‐Linearity in Partially Observed Time Series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 40(1), pages 43-62, March.
    • Handle: RePEc:bla:jorssc:v:40:y:1991:i:1:p:43-62
    DOI: 10.2307/2347904
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    Cited by:

    1. Milheiro-Oliveira, Paula, 2022. "An alternative sequential method for the state estimation of a partially observed SETAR(1) process," Statistics & Probability Letters, Elsevier, vol. 184(C).
    2. Beibei Zhang & Rong Chen, 2018. "Nonlinear Time Series Clustering Based on Kolmogorov-Smirnov 2D Statistic," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 394-421, October.
    3. Sonia Díaz & José Vilar, 2010. "Comparing Several Parametric and Nonparametric Approaches to Time Series Clustering: A Simulation Study," Journal of Classification, Springer;The Classification Society, vol. 27(3), pages 333-362, November.
    4. Sabkha, Saker & de Peretti, Christian & Hmaied, Dorra, 2019. "Nonlinearities in the oil effects on the sovereign credit risk: A self-exciting threshold autoregression approach," Research in International Business and Finance, Elsevier, vol. 50(C), pages 106-133.

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