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A time series paradox: Unit root tests perform poorly when data are cointegrated

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  • Reed, W. Robert
  • Smith, Aaron

Abstract

Cointegration among times series paradoxically makes it more likely that a unit test will reject the unit root null hypothesis on the individual series. This occurs because at least one series in the system has a negative moving average component.

Suggested Citation

  • Reed, W. Robert & Smith, Aaron, 2017. "A time series paradox: Unit root tests perform poorly when data are cointegrated," Economics Letters, Elsevier, vol. 151(C), pages 71-74.
  • Handle: RePEc:eee:ecolet:v:151:y:2017:i:c:p:71-74
    DOI: 10.1016/j.econlet.2016.12.005
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    References listed on IDEAS

    as
    1. Perron, Pierre & Rodriguez, Gabriel, 2003. "GLS detrending, efficient unit root tests and structural change," Journal of Econometrics, Elsevier, vol. 115(1), pages 1-27, July.
    2. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 39(3), pages 106-135.
    3. Carol H. Shiue & Wolfgang Keller, 2007. "Markets in China and Europe on the Eve of the Industrial Revolution," American Economic Review, American Economic Association, vol. 97(4), pages 1189-1216, September.
    4. Serena Ng & Pierre Perron, 2001. "LAG Length Selection and the Construction of Unit Root Tests with Good Size and Power," Econometrica, Econometric Society, vol. 69(6), pages 1519-1554, November.
    5. Phillips, P C B, 1991. "Optimal Inference in Cointegrated Systems," Econometrica, Econometric Society, vol. 59(2), pages 283-306, March.
    6. Perron, Pierre & Rodriguez, Gabriel, 2003. "GLS detrending, efficient unit root tests and structural change," Journal of Econometrics, Elsevier, vol. 115(1), pages 1-27, July.
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    More about this item

    Keywords

    Unit root testing; Cointegration; Augmented Dickey–Fuller test; Akaike Information Criterion (AIC); Bayesian Information Criterion (BIC); Modified Akaike Information Criterion (MAIC);

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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