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Partial unit root and linear spurious regression: A Monte Carlo simulation study

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  • Zhang, Lingxiang

Abstract

In this paper, we consider both the partial unit root and the near partial unit root processes in nonlinear transition autoregression models. Our simulations show that when these time series data are used in ordinary least squares regression, spurious regression occurs. However, if we re-estimate the regression by adding an AR(1) term, spurious regression can almost be eliminated.

Suggested Citation

  • Zhang, Lingxiang, 2013. "Partial unit root and linear spurious regression: A Monte Carlo simulation study," Economics Letters, Elsevier, vol. 118(1), pages 189-191.
    • Handle: RePEc:eee:ecolet:v:118:y:2013:i:1:p:189-191
    DOI: 10.1016/j.econlet.2012.10.018
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    References listed on IDEAS

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    Cited by:

    1. Frédéric Branger, Philippe Quirion, Julien Chevallier, 2017. "Carbon Leakage and Competitiveness of Cement and Steel Industries Under the EU ETS: Much Ado About Nothing," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    2. Tu, Yundong, 2017. "On spurious regressions with partial unit root processes," Economics Letters, Elsevier, vol. 150(C), pages 142-145.
    3. Zhang, Lingxiang, 2018. "Spurious regressions with high-order models: A reconsideration," Economics Letters, Elsevier, vol. 168(C), pages 70-72.

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    More about this item

    Keywords

    Partial unit root; Spurious regression; Monte Carlo simulation;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: 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
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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