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The "spurious regression problem" in the classical regression model framework

Author

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  • Gueorgui I. Kolev

    (EDHEC Business School)

Abstract

I analyse the "spurious regression problem" from the Classical Regression Model (CRM) point of view. Simulations show that the autocorrelation corrections suggested by the CRM, e.g., feasible generalised least squares, solve the problem. Estimators are unbiased, consistent, efficient and deliver correctly sized tests. Conversely, first differencing the data results in inefficiencies when the autoregressive parameter in the error process is less than one. I offer practical recommendations for handling cases suspected to be in the "spurious regression" class.

Suggested Citation

  • Gueorgui I. Kolev, 2011. "The "spurious regression problem" in the classical regression model framework," Economics Bulletin, AccessEcon, vol. 31(1), pages 925-937.
    • Handle: RePEc:ebl:ecbull:eb-10-00191
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    References listed on IDEAS

    as
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    7. Durbin, J, 1970. "Testing for Serial Correlation in Least-Squares Regression When Some of the Regressors are Lagged Dependent Variables," Econometrica, Econometric Society, vol. 38(3), pages 410-421, May.
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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. Daniel Ventosa-Santaulària & J. Eduardo Vera-Valdés & Alejandra I. Martínez-Olmos, 2016. "A comment on ‘resolving spurious regressions and serially correlated errors’," Empirical Economics, Springer, vol. 51(3), pages 1289-1298, November.

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    More about this item

    Keywords

    spurious regression; classical regression model; generalised least squares; autocorrelation corrections;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables

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