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Double-Length Artificial Regressions

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  • Russell Davidson
  • James G. MacKinnon

Abstract

Artificial linear regressions often provide a convenient way to calculate test statistics and estimate covariance matrices. This paper discusses one family of these regressions, called "double-length" because the number of observations in the artificial regression is twice the actual number of observations. These double-length regressions can be useful in a wide variety of situations. They are easy to calculate, and seem to have good properties when applied to samples of modest size. We first discuss how they are related to Gauss-Newton and squared-residuals regressions for nonlinear models, and then show how they may be used to test for functional form and other applications.

Suggested Citation

  • Russell Davidson & James G. MacKinnon, 1987. "Double-Length Artificial Regressions," Working Paper 691, Economics Department, Queen's University.
  • Handle: RePEc:qed:wpaper:691
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    References listed on IDEAS

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    Cited by:

    1. MacKinnon, James G, 1992. "Model Specification Tests and Artificial Regressions," Journal of Economic Literature, American Economic Association, vol. 30(1), pages 102-146, March.
    2. Badi Baltagi & Long Liu, 2014. "Testing for spatial lag and spatial error dependence using double length artificial regressions," Statistical Papers, Springer, vol. 55(2), pages 477-486, May.
    3. Davidson, Russell & MacKinnon, James G., 1989. "Testing for Consistency using Artificial Regressions," Econometric Theory, Cambridge University Press, vol. 5(3), pages 363-384, December.
    4. Le, Canh Quang & Li, Dong, 2008. "Double-Length Regression tests for testing functional forms and spatial error dependence," Economics Letters, Elsevier, vol. 101(3), pages 253-257, December.
    5. Thomas F. Crossley & Peter Levell & Stavros Poupakis, 2022. "Regression with an imputed dependent variable," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1277-1294, November.
    6. Benjamin Born & Jörg Breitung, 2011. "Simple regression‐based tests for spatial dependence," Econometrics Journal, Royal Economic Society, vol. 14(2), pages 330-342, July.
    7. Li Dong & Le Canh, 2010. "Nonlinearity and Spatial Lag Dependence: Tests Based on Double-Length Regressions," Journal of Time Series Econometrics, De Gruyter, vol. 2(1), pages 1-18, June.
    8. Baltagi, Badi H., 1997. "Testing linear and loglinear error components regressions against Box-Cox alternatives," Statistics & Probability Letters, Elsevier, vol. 33(1), pages 63-68, April.
    9. Badi H. Baltagi, 1999. "Specification Tests in Panel Data Models Using Artificial Regressions," Annals of Economics and Statistics, GENES, issue 55-56, pages 277-297.

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