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A comparison between Tau-d and the procedure TRAMO-SEATS is also included

Author

Listed:
  • Gabriel Rodriguez

    (Departamento de Economía - Pontificia Universidad Católica del Perú)

  • Dionisio Ramirez

    (Universidad Castilla La Mancha)

Abstract

Perron and Rodríguez (2003) claimed that their procedure to detect for additive outliers (Tau-d) is powerful even when we have departures from the unit root case. In this note, we use Monte-Carlo simulations to show that Tau-d is powerful when we have ARFIMA (p,d,q) errors. Using simulations, we calculate the expected number of additive outliers found in this context and the number of times that the approach Tau-d identi es the true location of the additive outliers. The results indicate that the power of the procedure Tau-d depends of the size of the additive outliers. When we have a DGP with big sized additive outliers the percentage of time that Tau-d detects correctly the location of the additive outliers is 100%.

Suggested Citation

  • Gabriel Rodriguez & Dionisio Ramirez, 2013. "A comparison between Tau-d and the procedure TRAMO-SEATS is also included," Documentos de Trabajo / Working Papers 2013-355, Departamento de Economía - Pontificia Universidad Católica del Perú.
    • Handle: RePEc:pcp:pucwps:wp00355
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    File URL: http://files.pucp.edu.pe/departamento/economia/DDD355.pdf
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    References listed on IDEAS

    as
    1. Pierre Perron & Gabriel Rodríguez, 2003. "Searching For Additive Outliers In Nonstationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 24(2), pages 193-220, March.
    2. Timothy J. Vogelsang, 1999. "Two Simple Procedures for Testing for a Unit Root When There are Additive Outliers," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(2), pages 237-252, March.
    3. Shin, Dong Wan & Sarkar, Sahadeb & Lee, Jong Hyup, 1996. "Unit root tests for time series with outliers," Statistics & Probability Letters, Elsevier, vol. 30(3), pages 189-197, October.
    4. Chan, Wai-sum, 1995. "Outliers and financial time series modelling: A cautionary note," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 39(3), pages 425-430.
    5. 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.
    6. Peter Burridge & A. M. Robert Taylor, 2006. "Additive Outlier Detection Via Extreme‐Value Theory," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(5), pages 685-701, September.
    7. Lucas, Andre, 1995. "An outlier robust unit root test with an application to the extended Nelson-Plosser data," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 153-173.
    8. Chareka, Patrick & Matarise, Florance & Turner, Rolf, 2006. "A test for additive outliers applicable to long-memory time series," Journal of Economic Dynamics and Control, Elsevier, vol. 30(4), pages 595-621, April.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Additive Outliers; ARFIMA Errors; Detection of Additive Out-liers.;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling

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