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Conditions for the numerical equality of the OLS, GLS and Amemiya–Cragg estimators

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  • Lu, Cuicui
  • Schmidt, Peter

Abstract

This paper extends previous results on the equality of OLS and GLS. We give conditions under which GLS based on two different variance matrices gives the same estimate, and also conditions under which GLS equals a GMM estimator.

Suggested Citation

  • Lu, Cuicui & Schmidt, Peter, 2012. "Conditions for the numerical equality of the OLS, GLS and Amemiya–Cragg estimators," Economics Letters, Elsevier, vol. 116(3), pages 538-540.
    • Handle: RePEc:eee:ecolet:v:116:y:2012:i:3:p:538-540
    DOI: 10.1016/j.econlet.2012.01.015
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    References listed on IDEAS

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    1. Baltagi, Badi H., 1989. "Applications of a necessary and sufficient condition for OLS to be BLUE," Statistics & Probability Letters, Elsevier, vol. 8(5), pages 457-461, October.
    2. Amemiya, Takeshi, 1983. "Partially generalized least squares and two-stage least squares estimators," Journal of Econometrics, Elsevier, vol. 23(2), pages 275-283, October.
    3. Cragg, John G, 1983. "More Efficient Estimation in the Presence of Heteroscedasticity of Unknown Form," Econometrica, Econometric Society, vol. 51(3), pages 751-763, May.
    4. Gourieroux, Christian & Monfort, Alain, 1980. "Sufficient Linear Structures: Econometric Applications," Econometrica, Econometric Society, vol. 48(5), pages 1083-1097, July.
    5. McAleer, Michael, 1992. "Efficient Estimation: The Rao-Zyskind Condition, Kruskal's Theorem and Ordinary Least Squares," The Economic Record, The Economic Society of Australia, vol. 68(200), pages 65-72, March.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Ordinary least squares; Generalized least squares; Amemiya–Cragg estimator;
    All these keywords.

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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