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Testing for unit roots based on sample autocovariances

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  • Chang, Jinyuan
  • Cheng, Guanghui
  • Yao, Qiwei

Abstract

We propose a new unit-root test for a stationary null hypothesis H0 against a unit-root alternative H1⁠. Our approach is nonparametric as H0 assumes only that the process concerned is I(0)⁠, without specifying any parametric forms. The new test is based on the fact that the sample autocovariance function converges to the finite population autocovariance function for an I(0) process, but diverges to infinity for a process with unit roots. Therefore, the new test rejects H0 for large values of the sample autocovariance function. To address the technical question of how large is large, we split the sample and establish an appropriate normal approximation for the null distribution of the test statistic. The substantial discriminative power of the new test statistic is due to the fact that it takes finite values under H0 and diverges to infinity under H1⁠. This property allows one to truncate the critical values of the test so that it has asymptotic power 1; it also alleviates the loss of power due to the sample-splitting. The test is implemented in R⁠.

Suggested Citation

  • Chang, Jinyuan & Cheng, Guanghui & Yao, Qiwei, 2022. "Testing for unit roots based on sample autocovariances," LSE Research Online Documents on Economics 114620, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:114620
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    File URL: http://eprints.lse.ac.uk/114620/
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    References listed on IDEAS

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    Cited by:

    1. Christis Katsouris, 2023. "Statistical Estimation for Covariance Structures with Tail Estimates using Nodewise Quantile Predictive Regression Models," Papers 2305.11282, arXiv.org, revised Jul 2023.

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    More about this item

    Keywords

    autocovariance; integrated processes; normal approximation; power-one test; sample-splitting; EP/V007556/1; OUP deal;
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    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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