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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 artificial regressions 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 artificail regression for regression models with heteroskedasticity of unknown form is introduced.

Suggested Citation

  • James G. MacKinnon & Russell Davidson, 1999. "Artificial Regressions," Working Paper 978, Economics Department, Queen's University.
    • Handle: RePEc:qed:wpaper:978
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    References listed on IDEAS

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    More about this item

    Keywords

    Heteroskedasticity; Gauss-Newton Regression; Specification Test;
    All these keywords.

    JEL classification:

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

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