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The KPSS Test with Outliers

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  • Jesús Otero
  • Jeremy Smith

Abstract

We investigate the effects of outliers on the KPSS tests. We find that for nonstationary series outliers induce spurious stationarity by lowering the power of these tests. The empirical size of these tests is also found to be sensitive to the location of the outlier. Copyright Springer Science+Business Media, Inc. 2005
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  • Jesús Otero & Jeremy Smith, 2007. "The KPSS Test with Outliers," Computational Economics, Springer;Society for Computational Economics, vol. 29(3), pages 423-423, May.
    • Handle: RePEc:kap:compec:v:29:y:2007:i:3:p:423-423
    DOI: 10.1007/s10614-006-9056-0
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    1. 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.
    2. Franses, Philip Hans & Haldrup, Niels, 1994. "The Effects of Additive Outliers on Tests for Unit Roots and Cointegration," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 471-478, October.
    3. Perron, Pierre, 1989. "The Great Crash, the Oil Price Shock, and the Unit Root Hypothesis," Econometrica, Econometric Society, vol. 57(6), pages 1361-1401, November.
    4. Van Dijk, Dick & Franses, Philip Hans & Lucas, Andre, 1999. "Testing for Smooth Transition Nonlinearity in the Presence of Outliers," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(2), pages 217-235, April.
    5. Mei-Yuan Chen, 2002. "Testing stationarity against unit roots and structural changes," Applied Economics Letters, Taylor & Francis Journals, vol. 9(7), pages 459-464.
    6. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
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    Cited by:

    1. Michieka, Nyakundi M. & Gearhart, Richard, 2015. "Oil price fluctuations and employment in Kern County: A Vector Error Correction approach," Energy Policy, Elsevier, vol. 87(C), pages 584-590.
    2. Michieka, Nyakundi M. & Fletcher, Jerald & Burnett, Wesley, 2013. "An empirical analysis of the role of China’s exports on CO2 emissions," Applied Energy, Elsevier, vol. 104(C), pages 258-267.
    3. P. S. Sephton, 2010. "Unit roots and purchasing power parity: another kick at the can," Applied Economics, Taylor & Francis Journals, vol. 42(27), pages 3439-3453.
    4. Zachmann, Georg, 2008. "Electricity wholesale market prices in Europe: Convergence?," Energy Economics, Elsevier, vol. 30(4), pages 1659-1671, July.
    5. Omid Bozorg-Haddad & Mohammad Solgi & Hugo A. Loáiciga, 2017. "Investigation of Climatic Variability with Hybrid Statistical Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(1), pages 341-353, January.
    6. Tayebi , Seyed Komail & Amini , Khaled Mohammad & Zamani , Zahra, 2012. "Inflation Determinants in Low and High Frequencies: An Implication of Spectral Analysis to Iran," Journal of Money and Economy, Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, vol. 7(1), pages 119-137, October.

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    More about this item

    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: 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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