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Subsampling Unit Root Tests for Heavy-Tailed Observations

Author

Listed:
  • Agnieszka Jach

    (Utah State University)

  • Piotr Kokoszka

    (Utah State University)

Abstract

It is shown that the subsampling methodology can be used to develop unit root tests when the noise sequence is heavy-tailed with infinite variance. Using least-squares residuals, we construct processes which approximately satisfy the null hypothesis and then, using subsampling, we approximate the null distribution of test statistics. We establish the asymptotic validity of this method and demonstrate its applicability in finite samples by means of a simulation study and a data example.

Suggested Citation

  • Agnieszka Jach & Piotr Kokoszka, 2004. "Subsampling Unit Root Tests for Heavy-Tailed Observations," Methodology and Computing in Applied Probability, Springer, vol. 6(1), pages 73-97, March.
    • Handle: RePEc:spr:metcap:v:6:y:2004:i:1:d:10.1023_b:mcap.0000012416.28866.c5
    DOI: 10.1023/B:MCAP.0000012416.28866.c5
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    References listed on IDEAS

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    Cited by:

    1. Arvanitis, Stelios, 2017. "A note on the limit theory of a Dickey–Fuller unit root test with heavy tailed innovations," Statistics & Probability Letters, Elsevier, vol. 126(C), pages 198-204.
    2. Matteo Barigozzi & Giuseppe Cavaliere & Lorenzo Trapani, 2024. "Inference in Heavy-Tailed Nonstationary Multivariate Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 119(545), pages 565-581, January.
    3. D. M. Mahinda Samarakoon & Keith Knight, 2009. "A Note on Unit Root Tests with Infinite Variance Noise," Econometric Reviews, Taylor & Francis Journals, vol. 28(4), pages 314-334.
    4. Jin, Hao & Tian, Zheng & Qin, Ruibing, 2009. "Subsampling tests for the mean change point with heavy-tailed innovations," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(7), pages 2157-2166.
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    6. Jin, Hao & Zhang, Jinsuo, 2010. "Subsampling tests for variance changes in the presence of autoregressive parameter shifts," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2255-2265, November.

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