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A Residual-Based ADF Test for Stationary Cointegration in I (2) Settings

Author

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  • Javier Gómez Biscarri
  • Javier Hualde

Abstract

We propose a residual-based augmented Dickey-Fuller (ADF) test statistic that allows for detection of stationary cointegration within a system that may contain both I (2) and I (1) observables. The test is also consistent under the alternative of multicointegration, where first differences of the I (2) observables enter the cointegrating relationships. We find the null limiting distribution of this statistic and justify why our proposal improves over related approaches. Critical values are computed for a variety of situations. Additionally, building on this ADF test statistic, we propose a procedure to test the null of no stationary cointegration which overcomes the drawback, suffered by any residual-based method, of the lack of power with respect to some relevant alternatives. Finally, a Monte Carlo experiment is carried out and an empirical application is provided as an illustrative example.

Suggested Citation

  • Javier Gómez Biscarri & Javier Hualde, 2014. "A Residual-Based ADF Test for Stationary Cointegration in I (2) Settings," Working Papers 779, Barcelona Graduate School of Economics.
  • Handle: RePEc:bge:wpaper:779
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    Cited by:

    1. Chun Deng & Jie-Fang Dong, 2016. "Coal Consumption Reduction in Shandong Province: A Dynamic Vector Autoregression Model," Sustainability, MDPI, Open Access Journal, vol. 8(9), pages 1-16, August.
    2. Xu, Bin & Lin, Boqiang, 2016. "Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model," Applied Energy, Elsevier, vol. 161(C), pages 375-386.

    More about this item

    Keywords

    systems; stationary cointegration; multicointegration; residual-based tests;

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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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