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Spectral approach to parameter-free unit root testing

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  • Bailey, Natalia
  • Giraitis, Liudas

Abstract

A relatively simple frequency-type testing procedure for unit root potentially contaminated by an additive stationary noise is introduced, which encompasses general settings and allows for linear trends. The proposed test for unit root versus stationarity is based on a finite number of periodograms computed at low Fourier frequencies. It is not sensitive to the selection of tuning parameters defining the range of frequencies so long as they are in the vicinity of zero. The test does not require augmentation, has parameter-free non-standard asymptotic distribution and is correctly sized. The consistency rate under the alternative of stationarity reveals the relation between the power of the test and the long-run variance of the process. The finite sample performance of the test is explored in a Monte Carlo simulation study, and its empirical application suggests rejection of the unit root hypothesis for some of the Nelson–Plosser time series.

Suggested Citation

  • Bailey, Natalia & Giraitis, Liudas, 2016. "Spectral approach to parameter-free unit root testing," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 4-16.
    • Handle: RePEc:eee:csdana:v:100:y:2016:i:c:p:4-16
    DOI: 10.1016/j.csda.2015.05.002
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    References listed on IDEAS

    as
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    Cited by:

    1. Gennadi Gromykov & Mohamedou Ould Haye & Anne Philippe, 2018. "A frequency-domain test for long range dependence," Statistical Inference for Stochastic Processes, Springer, vol. 21(3), pages 513-526, October.

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    More about this item

    Keywords

    Unit root test; Additive noise; Parameter-free distribution;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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