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The Linear and Non-displaced Estimator in Multiple Regression

Author

Listed:
  • Constantin ANGHELACHE

    („Artifex” University of Bucharest / Academy of Economic Studies, Bucharest)

  • Vergil VOINEAGU

    (Academy of Economic Studies, Bucharest)

  • Alexandru MANOLE

    („Artifex” University of Bucharest)

  • Diana Valentina SOARE

    (Academy of Economic Studies, Bucharest)

  • Ligia PRODAN

    („Dimitrie Cantemir” Christian University, Bucharest)

Abstract

Under the hypotheses IA and IB, OLS estimators are both linear and stationary. For it to provide the same minimum variance of all linear and stationary estimators and to take part of BLUE, it is necessary that the classical assumptions IIB and IIC should be available. As in the case of two-variable regression, this means that the residual factors has to be homoschedastic and non-autocorrelated.

Suggested Citation

  • Constantin ANGHELACHE & Vergil VOINEAGU & Alexandru MANOLE & Diana Valentina SOARE & Ligia PRODAN, 2013. "The Linear and Non-displaced Estimator in Multiple Regression," Romanian Statistical Review Supplement, Romanian Statistical Review, vol. 61(2), pages 161-166, May.
    • Handle: RePEc:rsr:supplm:v:61:y:2013:i:2:p:161-166
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    More about this item

    Keywords

    correlation; residual; regression; inference; parameter;
    All these keywords.

    JEL classification:

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

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