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Trend and initial condition in stationarity tests: the asymptotic analysis

Author

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  • Anton Skrobotov

    (Gaidar Institute for Economic Policy)

Abstract

In this paperwe investigate the behavior of stationarity tests proposed by Muller (2005) and Harris et al. (2007) with uncertainty over the trend and/or initial condition. As dierent tests are e cient for dierent magnitudes of local trend and initial condition, following Harvey et al. (2012) we propose decision rule based on the rejection of null hypothesis for multiple tests. Additionally, we propose a modi cation of this decision rule, relying on additional information about the magnitudes of the local trend and/or the initial condition that is obtained through pre-testing. The resulting modification has satisfactory size properties under both uncertainty types.

Suggested Citation

  • Anton Skrobotov, 2012. "Trend and initial condition in stationarity tests: the asymptotic analysis," Working Papers 0048, Gaidar Institute for Economic Policy, revised 2013.
  • Handle: RePEc:gai:wpaper:0048
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    File URL: http://www.iep.ru/files/RePEc/gai/wpaper/0048Skrobotov.pdf
    File Function: Revised version, 2012
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    References listed on IDEAS

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    1. David I. Harvey & Stephen J. Leybourne & A. M. Robert Taylor, 2010. "The impact of the initial condition on robust tests for a linear trend," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(4), pages 292-302, July.
    2. Bunzel, Helle & Vogelsang, Timothy J., 2005. "Powerful Trend Function Tests That Are Robust to Strong Serial Correlation, With an Application to the Prebisch-Singer Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 381-394, October.
    3. Whitney K. Newey & Kenneth D. West, 1994. "Automatic Lag Selection in Covariance Matrix Estimation," Review of Economic Studies, Oxford University Press, vol. 61(4), pages 631-653.
    4. Kwiatkowski, Denis & Phillips, Peter C. B. & Schmidt, Peter & Shin, Yongcheol, 1992. "Testing the null hypothesis of stationarity against the alternative of a unit root : How sure are we that economic time series have a unit root?," Journal of Econometrics, Elsevier, vol. 54(1-3), pages 159-178.
    5. Harris, David & Leybourne, Stephen & McCabe, Brendan, 2007. "Modified Kpss Tests For Near Integration," Econometric Theory, Cambridge University Press, vol. 23(02), pages 355-363, April.
    6. Mohitosh Kejriwal & Pierre Perron, 2010. "A sequential procedure to determine the number of breaks in trend with an integrated or stationary noise component," Journal of Time Series Analysis, Wiley Blackwell, vol. 31(5), pages 305-328, September.
    7. Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2012. "Testing for unit roots in the presence of uncertainty over both the trend and initial condition," Journal of Econometrics, Elsevier, vol. 169(2), pages 188-195.
    8. Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2007. "A simple, robust and powerful test of the trend hypothesis," Journal of Econometrics, Elsevier, vol. 141(2), pages 1302-1330, December.
    9. Harvey, David I. & Leybourne, Stephen J. & Taylor, A.M. Robert, 2009. "Unit Root Testing In Practice: Dealing With Uncertainty Over The Trend And Initial Condition," Econometric Theory, Cambridge University Press, vol. 25(03), pages 587-636, June.
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    Cited by:

    1. Anton Skrobotov, 2013. "Local Structural Trend Break in Stationarity Testing," Working Papers 0074, Gaidar Institute for Economic Policy, revised 2013.

    More about this item

    Keywords

    Stationarity test; KPSS test; uncertainty over the trend; uncertainty over the initial condition; size distortion; intersection of rejection decision rule.;

    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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