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

Author

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  • Osabuohien-Irabor Osarumwense

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

  • Julian I. Mbegbu

    (University of Benin, Benin City, Nigeria)

Abstract

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

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    More about this item

    Keywords

    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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