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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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    1. Marmol, Francesc, 1996. "Nonsense Regressions between Integrated Processes of Different Orders," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 58(3), pages 525-536, August.
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    8. Mehmet Caner & Bruce E. Hansen, 2001. "Threshold Autoregression with a Unit Root," Econometrica, Econometric Society, vol. 69(6), pages 1555-1596, November.
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    Cited by:

    1. Tu, Yundong, 2017. "On spurious regressions with partial unit root processes," Economics Letters, Elsevier, vol. 150(C), pages 142-145.

    More about this item

    Keywords

    Partial unit root; Spurious regression; Monte Carlo simulation;

    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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