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Spurious regressions with high-order models: A reconsideration

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  • Zhang, Lingxiang

Abstract

This paper reconsiders spurious regressions with high-order processes by using a balanced regression approach. Results show that if the balanced orders adopted in a regression model are greater than the truly largest lag of regressand and regressor data generation processes, then spurious regressions can be eliminated.

Suggested Citation

  • Zhang, Lingxiang, 2018. "Spurious regressions with high-order models: A reconsideration," Economics Letters, Elsevier, vol. 168(C), pages 70-72.
    • Handle: RePEc:eee:ecolet:v:168:y:2018:i:c:p:70-72
    DOI: 10.1016/j.econlet.2018.04.007
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    References listed on IDEAS

    as
    1. Zhang, Lingxiang, 2013. "Partial unit root and linear spurious regression: A Monte Carlo simulation study," Economics Letters, Elsevier, vol. 118(1), pages 189-191.
    2. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
    3. McCallum, Bennett T., 2010. "Is the spurious regression problem spurious?," Economics Letters, Elsevier, vol. 107(3), pages 321-323, June.
    4. Sollis, Robert, 2011. "Spurious regression: A higher-order problem," Economics Letters, Elsevier, vol. 111(2), pages 141-143, May.
    5. Tu, Yundong, 2017. "On spurious regressions with partial unit root processes," Economics Letters, Elsevier, vol. 150(C), pages 142-145.
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    More about this item

    Keywords

    Spurious regression; High-order models; Balanced regression;
    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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