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Non-linear unit root testing in the presence of heavy-tailed innovation processes

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  • Steve Cook

    (Swansea University)

Abstract

The literature concerning the impact of heavy-tailed innovations upon unit root tests is extended via analysis of the finite-sample distribution and size of the non-linear unit of Kapetanios et al. (2003) in the presence of alternative finite and infinite variance innovation processes. Simulation results obtained show the test to exhibit a degree of oversizing far in excess of that previously noted for the linear Dickey-Fuller (1979) test.

Suggested Citation

  • Steve Cook, 2008. "Non-linear unit root testing in the presence of heavy-tailed innovation processes," Economics Bulletin, AccessEcon, vol. 3(38), pages 1-10.
    • Handle: RePEc:ebl:ecbull:eb-08c20043
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    References listed on IDEAS

    as
    1. Deo, Rohit S., 2000. "On estimation and testing goodness of fit for m-dependent stable sequences," Journal of Econometrics, Elsevier, vol. 99(2), pages 349-372, December.
    2. Kapetanios, George & Shin, Yongcheol & Snell, Andy, 2003. "Testing for a unit root in the nonlinear STAR framework," Journal of Econometrics, Elsevier, vol. 112(2), pages 359-379, February.
    3. Liew, Venus Khim-sen & Baharumshah, Ahmad Zubaidi & Chong, Terence Tai-leung, 2004. "Are Asian real exchange rates stationary?," Economics Letters, Elsevier, vol. 83(3), pages 313-316, June.
    4. Loretan, Mico & Phillips, Peter C. B., 1994. "Testing the covariance stationarity of heavy-tailed time series: An overview of the theory with applications to several financial datasets," Journal of Empirical Finance, Elsevier, vol. 1(2), pages 211-248, January.
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    Cited by:

    1. Umair Khalil & Alamgir & Amjad Ali & Dost Muhammad Khan & Sajjad Ahmad Khan & Zardad Khan, 2016. "Unit Root Testing and Estimation in Nonlinear ESTAR Models with Normal and Non-Normal Errors," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-11, November.

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    More about this item

    Keywords

    heavy-tailed distributions;

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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