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Revisiting Error‐Autocorrelation Correction: Common Factor Restrictions and Granger Non‐Causality

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  • Anya McGuirk
  • Aris Spanos

Abstract

The paper questions the appropriateness of the practice known as ‘error‐autocorrelation correcting’ in linear regression, by showing that adopting an AR(1) error formulation is equivalent to assuming that the regressand does not Granger cause any of the regressors. This result is used to construct a new test for the common factor restrictions, as well as investigate – using Monte Carlo simulations – other potential sources of unreliability of inference resulting from this practice. The main conclusion is that when the Granger cause restriction is false, the ordinary least square and generalized least square estimators are biased and inconsistent, and using autocorrelation‐consistent standard errors does not improve the reliability of inference.

Suggested Citation

  • Anya McGuirk & Aris Spanos, 2009. "Revisiting Error‐Autocorrelation Correction: Common Factor Restrictions and Granger Non‐Causality," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(2), pages 273-294, April.
    • Handle: RePEc:bla:obuest:v:71:y:2009:i:2:p:273-294
    DOI: 10.1111/j.1468-0084.2008.00538.x
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    7. Aris Spanos & Anya McGuirk, 2001. "The Model Specification Problem from a Probabilistic Reduction Perspective," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 83(5), pages 1168-1176.
    8. Aris Spanos, 2006. "Revisiting the omitted variables argument: Substantive vs. statistical adequacy," Journal of Economic Methodology, Taylor & Francis Journals, vol. 13(2), pages 179-218.
    9. Spanos, Aris & McGuirk, Anya, 2002. "The problem of near-multicollinearity revisited: erratic vs systematic volatility," Journal of Econometrics, Elsevier, vol. 108(2), pages 365-393, June.
    10. Hoover, Kevin D, 1988. "On the Pitfalls of Untested Common-Factor Restrictions: The Case of the Inverted Fisher Hypothesis," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 50(2), pages 125-138, May.
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    2. Spanos, Aris, 2010. "Statistical adequacy and the trustworthiness of empirical evidence: Statistical vs. substantive information," Economic Modelling, Elsevier, vol. 27(6), pages 1436-1452, November.
    3. Piper, Alan T., 2012. "A Happiness Test of Human Capital Theory," MPRA Paper 43496, University Library of Munich, Germany.

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