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Double Unit Roots Testing, GLS-detrending and Uncertainty over the Initial Conditions

Author

Listed:
  • Anton Skrobotov

    (Gaidar Institute for Economic Policy)

Abstract

This paper proposes the extension of the Hasza and Fuller (1979) test for double unit roots based on GLS-detrending. The limiting distribution of this test is obtained under local to unity representation and coincides with the distribution of the conventional test in the absence of a deterministic component. The proposed test has both better asymptotic and _nite sample properties in comparison to tests based on OLS-detrending. This paper proposes modi_ed information criteria for the implementation of the proposed test for double unit roots in _nite samples in which an additional term is incorporated into the penalty function. This provides better size control under various data generating processes, especially for strongly negative moving average components. This paper also analyzes the power behavior of tests under non-negligible initial conditions and proposes union of rejection testing strategy of three tests following the Harvey et al. (2009) approach. This strategy is more robust across various magnitudes of the initial conditions and eliminates large power losses that occur due to the use of only one of the tests.

Suggested Citation

  • Anton Skrobotov, 2013. "Double Unit Roots Testing, GLS-detrending and Uncertainty over the Initial Conditions," Working Papers 0083, Gaidar Institute for Economic Policy, revised 2013.
    • Handle: RePEc:gai:wpaper:0083
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    References listed on IDEAS

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    More about this item

    Keywords

    Double unit roots test; GLS-detrending; lag length selection; information criteria; uncertainty over the initial conditions; union of rejection;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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