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Power and Size analysis of Co-integration tests in Conditional Heteroskedascity: A Monte Carlo Simulation


  • Osabuohien-Irabor Osarumwense

    (Ambrose Alli University (A.A.U), Ekpoma, Edo, Nigeria)

  • Julian I. Mbegbu

    (University of Benin, Benin City, Nigeria)


This paper investigates the finite sample performance of power and size properties of several major co-integration tests using simulation analysis. These tests include; the co-integration Regression Durbin-Watson test (CRDW), Eagle-Granger test, Dicky Fuller unit root test with () statistics, Johansen likelihood ratio tests, and Phillips-Ouliaris test. Comparisons of tests are evaluated based on the proportion of rejects of the hypothesis of a no co-integration. This study answers the question of which co-integration test is better, particularly between the Eagle-Granger two-step test and the Johansen’s tests for co-integration, when the sets of parameters in models are persistence and spiky. The bivariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH(1,1)) model with Gaussian innovations, is used in the data generating process (DGP). Our simulation results reveal that there is size distortion in the different co-integration test considered. The Eagle-Granger two-step test shows good robustness with respect to heteroskedasticity for the different sample sizes applied. However, the Johansen’s test for co-integration still proves to be powerful in capturing co-integration relationship, particularly for large sample when the co-integration innovations are Gaussian.

Suggested Citation

  • Osabuohien-Irabor Osarumwense & Julian I. Mbegbu, 2017. "Power and Size analysis of Co-integration tests in Conditional Heteroskedascity: A Monte Carlo Simulation," Romanian Statistical Review, Romanian Statistical Review, vol. 65(3), pages 17-34, September.
  • Handle: RePEc:rsr:journl:v:65:y:2017:i:3:p:17-34

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    References listed on IDEAS

    1. Mallory Mindy & Lence Sergio H., 2012. "Testing for Cointegration in the Presence of Moving Average Errors," Journal of Time Series Econometrics, De Gruyter, vol. 4(2), pages 1-68, November.
    2. Cavaliere, Giuseppe & Rahbek, Anders & Taylor, A.M. Robert, 2010. "Testing for co-integration in vector autoregressions with non-stationary volatility," Journal of Econometrics, Elsevier, vol. 158(1), pages 7-24, September.
    3. Hyungsun Chloe Cho & Miguel D. Ramirez, 2016. "Money Demand in Korea: A Cointegration Analysis, 1973-2014," Business and Economic Research, Macrothink Institute, vol. 6(1), pages 96-110, June.
    4. Sargan, John Denis & Bhargava, Alok, 1983. "Testing Residuals from Least Squares Regression for Being Generated by the Gaussian Random Walk," Econometrica, Econometric Society, vol. 51(1), pages 153-174, January.
    5. David H. Bernstein & Bent Nielsen, 2019. "Asymptotic Theory for Cointegration Analysis When the Cointegration Rank Is Deficient," Econometrics, MDPI, Open Access Journal, vol. 7(1), pages 1-24, January.
    6. Turgut Tursoy & Faisal Faisal, 2017. "Re-testing for financial integration of the Turkish Stock Market and the US Stock Market: An Evidence from co-integration and error correction models," Romanian Statistical Review, Romanian Statistical Review, vol. 65(2), pages 43-55, June.
    7. Christopher Krauss & Klaus Herrmann, 2017. "On the Power and Size Properties of Cointegration Tests in the Light of High-Frequency Stylized Facts," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 10(1), pages 1-24, February.
    8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    9. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    10. Müller, Ulrich K. & Watson, Mark W., 2013. "Low-frequency robust cointegration testing," Journal of Econometrics, Elsevier, vol. 174(2), pages 66-81.
    11. Raul Caruso & Marco Di Domizio, 2016. "Interdependence between US and European military spending: a panel cointegration analysis (1988-2013)," Applied Economics Letters, Taylor & Francis Journals, vol. 23(4), pages 302-305, March.
    12. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
    13. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    More about this item


    Co-integration test; Size; Monte-Carlos Simulation; Power; Heteroskedasticity;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • 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


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