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Testing for seasonal unit roots by frequency domain regression

Author

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  • Chambers, Marcus J.
  • Ercolani, Joanne S.
  • Taylor, A.M. Robert

Abstract

This paper develops univariate seasonal unit root tests based on spectral regression estimators. An advantage of the frequency domain approach is that it enables serial correlation to be treated non-parametrically. We demonstrate that our proposed statistics have pivotal limiting distributions under both the null and near seasonally integrated alternatives when we allow for weak dependence in the driving shocks. This is in contrast to the popular seasonal unit root tests of, among others, Hylleberg et al. (1990) which treat serial correlation parametrically via lag augmentation of the test regression. Our analysis allows for (possibly infinite order) moving average behaviour in the shocks. The size and power properties of our proposed frequency domain regression-based tests are explored and compared for the case of quarterly data with those of the tests of Hylleberg et al. (1990) in simulation experiments.

Suggested Citation

  • Chambers, Marcus J. & Ercolani, Joanne S. & Taylor, A.M. Robert, 2014. "Testing for seasonal unit roots by frequency domain regression," Journal of Econometrics, Elsevier, vol. 178(P2), pages 243-258.
    • Handle: RePEc:eee:econom:v:178:y:2014:i:p2:p:243-258
    DOI: 10.1016/j.jeconom.2013.08.025
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    4. Burak Eroglu & Kemal Caglar Gogebakan & Mirza Trokic, 2017. "Fractional Seasonal Variance Ratio Unit Root Tests," Working Papers 1707, The Center for Financial Studies (CEFIS), Istanbul Bilgi University.
    5. Sheng-Hung Chen & Song-Zan Chiou-Wei & Zhen Zhu, 2022. "Stochastic seasonality in commodity prices: the case of US natural gas," Empirical Economics, Springer, vol. 62(5), pages 2263-2284, May.

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    More about this item

    Keywords

    Seasonal unit root tests; Moving average; Frequency domain regression; Spectral density estimator; Brownian motion;
    All these keywords.

    JEL classification:

    • 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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