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Portmanteau-type tests for unit-root and cointegration

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  • Zhang, Rongmao
  • Chan, Ngai Hang

Abstract

This paper proposes a new portmanteau-type statistic by combining several lags of the sample autocorrelations to test for the presence of a unit-root of an autoregressive model. The proposed method is nonparametric in nature, which is model free and easy to implement. It avoids modeling the fitted residuals and does not require estimation of nuisance parameters, as commonly done in the augmented Dickey–Fuller or Phillips–Perron procedure. Asymptotic properties of the test are established under general stationary conditions on the noises. Finite sample studies are also reported to illustrate the superior power of the proposed method. Applications to test for cointegration are also given.

Suggested Citation

  • Zhang, Rongmao & Chan, Ngai Hang, 2018. "Portmanteau-type tests for unit-root and cointegration," Journal of Econometrics, Elsevier, vol. 207(2), pages 307-324.
    • Handle: RePEc:eee:econom:v:207:y:2018:i:2:p:307-324
    DOI: 10.1016/j.jeconom.2018.08.004
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    2. Feng, Zongbao & Chen, Weiya & Liu, Yang & Chen, Hongyu & Skibniewski, Mirosław J., 2023. "Long-term equilibrium relationship analysis and energy-saving measures of metro energy consumption and its influencing factors based on cointegration theory and an ARDL model," Energy, Elsevier, vol. 263(PD).

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    More about this item

    Keywords

    Autoregressive processes; Cointegration; Portmanteau test; Sample covariance; Unit-root;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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