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A Note on the Size of the ADF Test with Additive Outliers and Fractional Errors. A Reapraisal about the (Non) Stationarity of the Latin-American Inflation Series

Author

Listed:
  • Gabriel Rodriguez

    (Departamento de Economía - Pontificia Universidad Católica del Perú)

  • Dionisio Ramirez

    (Universidad Castilla La Mancha)

Abstract
This note analyzes the empirical size of the augmented Dickey and Fuller (ADF) statistic proposed by Perron and Rodríguez (2003) when the errors are fractional. This ADF is based on a searching procedure for additive outliers based on rst-differences of the data named Tau-d. Simulations show that empirical size of the ADF is not affected by fractional errors con rming the claim of Perron and Rodríguez (2003) that the procedure Tau-d is robust to departures of the unit root framework. In particular the results show low sensitivity of the size of the ADF statistic respect to the fractional parameter (d). However, as expected, when there is strong negative moving average autocorrelation or negative autoregressive autocorrelation, the ADF statistic is oversized. These difficulties are xed when sample increases (from T = 100 to T = 200). Empirical application to eight quarterly Latin-American ination series is also provided showing the importance of taking into account dummy variables for the detected additive outliers.

Suggested Citation

  • Gabriel Rodriguez & Dionisio Ramirez, 2013. "A Note on the Size of the ADF Test with Additive Outliers and Fractional Errors. A Reapraisal about the (Non) Stationarity of the Latin-American Inflation Series," Documentos de Trabajo / Working Papers 2013-357, Departamento de Economía - Pontificia Universidad Católica del Perú.
    • Handle: RePEc:pcp:pucwps:wp00357
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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. John Y. Campbell & Pierre Perron, 1991. "Pitfalls and Opportunities: What Macroeconomists Should Know about Unit Roots," NBER Chapters, in: NBER Macroeconomics Annual 1991, Volume 6, pages 141-220, National Bureau of Economic Research, Inc.
    3. 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.
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    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. Pierre Perron & Serena Ng, 1996. "Useful Modifications to some Unit Root Tests with Dependent Errors and their Local Asymptotic Properties," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 63(3), pages 435-463.
    10. 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.
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    Keywords

    Additive Outliers; ARFIMA Errors; ADF test;
    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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