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Selecting optimal lag order in Ljung–Box test

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  • Hassani, Hossein
  • Yeganegi, Mohammad Reza

Abstract

Theoretical results and empirical evidences indicate that the Ljung–Box test is sensitive to the number of lags (H) involved in the test. In time series literature, different values are suggested for H. This paper is concerned with the selecting optimal number of lags H in Ljung–Box test such that the actual size of the test does not exceed the test’s level while the power of the test does not fall under specific value. A simulation study is employed to investigate the effect of selecting an improper values of H on the actual size and power of the Ljung–Box test. The results confirm that an optimal value of H depends on the time series’ length as well as the test’s level. The comparison results with currently used approaches in the literature confirm that the commonly used techniques are not suggesting a proper value for H.

Suggested Citation

  • Hassani, Hossein & Yeganegi, Mohammad Reza, 2020. "Selecting optimal lag order in Ljung–Box test," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    • Handle: RePEc:eee:phsmap:v:541:y:2020:i:c:s0378437119320618
    DOI: 10.1016/j.physa.2019.123700
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    References listed on IDEAS

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    1. Hassani, Hossein, 2010. "A note on the sum of the sample autocorrelation function," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1601-1606.
    2. Hassani, Hossein & Yeganegi, Mohammad Reza, 2019. "Sum of squared ACF and the Ljung–Box statistics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 81-86.
    3. Hassani, Hossein & Leonenko, Nikolai & Patterson, Kerry, 2012. "The sample autocorrelation function and the detection of long-memory processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6367-6379.
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    Cited by:

    1. Liu, Hui & Yang, Rui & Wang, Tiantian & Zhang, Lei, 2021. "A hybrid neural network model for short-term wind speed forecasting based on decomposition, multi-learner ensemble, and adaptive multiple error corrections," Renewable Energy, Elsevier, vol. 165(P1), pages 573-594.
    2. Yang, Rui & Liu, Hui & Nikitas, Nikolaos & Duan, Zhu & Li, Yanfei & Li, Ye, 2022. "Short-term wind speed forecasting using deep reinforcement learning with improved multiple error correction approach," Energy, Elsevier, vol. 239(PB).
    3. Tziolis, Georgios & Spanias, Chrysovalantis & Theodoride, Maria & Theocharides, Spyros & Lopez-Lorente, Javier & Livera, Andreas & Makrides, George & Georghiou, George E., 2023. "Short-term electric net load forecasting for solar-integrated distribution systems based on Bayesian neural networks and statistical post-processing," Energy, Elsevier, vol. 271(C).
    4. Masoomeh Forughi & Zohreh Shishebor & Atefeh Zamani, 2022. "Portmanteau tests for generalized integer-valued autoregressive time series models," Statistical Papers, Springer, vol. 63(4), pages 1163-1185, August.

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