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Artificial Regressions

Author

Listed:
  • James G. MacKinnon

    (Queen's University)

  • Russell Davidson

    (McGill University)

Abstract

Associated with every popular nonlinear estimation method is at least one ``artificial'' linear regression. We define an artificial regression in terms of three conditions that it must satisfy. Then we show how artificialregressions can be useful for numerical optimization, testing hypotheses, and computing parameter estimates. Several existing artificial regressions are discussed and are shown to satisfy the defining conditions, and a new artificial regression for regression models with heteroskedasticity of unknown form is introduced.

Suggested Citation

  • James G. MacKinnon & Russell Davidson, 2001. "Artificial Regressions," Working Paper 1038, Economics Department, Queen's University.
  • Handle: RePEc:qed:wpaper:1038
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    More about this item

    Keywords

    artificial regression; LM test; specification test; Gauss-Newton regression; one-step estimation; OPG regression; double-length regression; binary response model;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

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