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The fragility of the KPSS stationarity test

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  • Nunzio Cappuccio
  • Diego Lubian

Abstract

Stationarity tests exhibit extreme size distortions if the observable process is stationary yet highly persistent. In this paper we provide a theoretical explanation for the size distortion of the KPSS test for DGPs with a broad range of first order autocorrelation coefficient. Considering a near-integrated, nearly stationary process we show that the asymptotic distribution of the test contains an additional term, which can potentially explain the amount of size distortion documented in previous simulation studies.
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  • Nunzio Cappuccio & Diego Lubian, 2010. "The fragility of the KPSS stationarity test," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(2), pages 237-253, June.
    • Handle: RePEc:spr:stmapp:v:19:y:2010:i:2:p:237-253
    DOI: 10.1007/s10260-010-0130-3
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    Cited by:

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    2. Majid M. Al-Sadoon, 2014. "A general theory of rank testing," Economics Working Papers 1411, Department of Economics and Business, Universitat Pompeu Fabra, revised Feb 2015.
    3. Tadeusz Bednarski, 2010. "Fréchet differentiability in statistical inference for time series," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 19(4), pages 517-528, November.
    4. Peter Sephton, 2017. "Finite Sample Critical Values of the Generalized KPSS Stationarity Test," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 161-172, June.

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    More about this item

    Keywords

    KPSS stationarity test; Size distortion; Nearly-white noise nearly integrated model;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • 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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