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Improvement of the quasi‐likelihood ratio test in ARMA models: some results for bootstrap methods

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  • A. Canepa
  • L. G. Godfrey

Abstract

. Quasi‐likelihood ratio tests for autoregressive moving‐average (ARMA) models are examined. The ARMA models are stationary and invertible with white‐noise terms that are not restricted to be normally distributed. The white‐noise terms are instead subject to the weaker assumption that they are independently and identically distributed with an unspecified distribution. Bootstrap methods are used to improve control of the finite sample significance levels. The bootstrap is used in two ways: first, to approximate a Bartlett‐type correction; and second, to estimate the p‐value of the observed test statistic. Some simulation evidence is provided. The bootstrap p‐value test emerges as the best performer in terms of controlling significance levels.

Suggested Citation

  • A. Canepa & L. G. Godfrey, 2007. "Improvement of the quasi‐likelihood ratio test in ARMA models: some results for bootstrap methods," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(3), pages 434-453, May.
  • Handle: RePEc:bla:jtsera:v:28:y:2007:i:3:p:434-453
    DOI: 10.1111/j.1467-9892.2006.00518.x
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    Cited by:

    1. Fernanda Maria Müller & Fábio M Bayer, 2017. "Improved two-component tests in Beta-Skew-t-EGARCH models," Economics Bulletin, AccessEcon, vol. 37(4), pages 2364-2373.
    2. Canepa Alessandra, 2022. "Small Sample Adjustment for Hypotheses Testing on Cointegrating Vectors," Journal of Time Series Econometrics, De Gruyter, vol. 14(1), pages 51-85, January.
    3. Canepa, Alessandra, 2016. "A note on Bartlett correction factor for tests on cointegrating relations," Statistics & Probability Letters, Elsevier, vol. 110(C), pages 296-304.
    4. Canepa, Alessandra, 2020. "Bootstrap Bartlett Adjustment for Hypotheses Testing on Cointegrating Vectors," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202006, University of Turin.

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