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Partially one-sided semiparametric inference for trending persistent and antipersistent processes

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  • Abadir, Karim M.
  • Distaso, Walter
  • Giraitis, Liudas

Abstract

Hypothesis testing in models allowing for trending processes that are possibly nonstationary and non-Gaussian is considered. Using semiparametric estimators, joint hypothesis testing for these processes is developed, taking into account the one-sided nature of typical hypotheses on the persistence parameter in order to gain power. The results are applicable for a wide class of processes and are easy to implement. They are illustrated with an application to the dynamics of GDP.

Suggested Citation

  • Abadir, Karim M. & Distaso, Walter & Giraitis, Liudas, 2024. "Partially one-sided semiparametric inference for trending persistent and antipersistent processes," Econometrics and Statistics, Elsevier, vol. 30(C), pages 1-14.
    • Handle: RePEc:eee:ecosta:v:30:y:2024:i:c:p:1-14
    DOI: 10.1016/j.ecosta.2021.12.007
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    References listed on IDEAS

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    1. Abadir, Karim M. & Distaso, Walter, 2007. "Testing joint hypotheses when one of the alternatives is one-sided," Journal of Econometrics, Elsevier, vol. 140(2), pages 695-718, October.
    2. Dolado Juan J. & Gonzalo Jesus & Mayoral Laura, 2008. "Wald Tests of I(1) against I(d) Alternatives: Some New Properties and an Extension to Processes with Trending Components," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 12(4), pages 1-35, December.
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    9. Abadir, Karim M. & Distaso, Walter & Giraitis, Liudas, 2011. "An I(d) model with trend and cycles," Journal of Econometrics, Elsevier, vol. 163(2), pages 186-199, August.
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    More about this item

    Keywords

    fractional integration and trends; partially one-sided joint hypotheses; fully-extended local Whittle estimation;
    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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