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Artificial Regressions

Author

Listed:
  • Davidson, R.
  • MacKinnon
  • J.G.

Abstract

Associated with every popular nonlinear estimationmethod is at least ont "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 paremeter 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

  • Davidson, R. & MacKinnon & J.G., 1999. "Artificial Regressions," G.R.E.Q.A.M. 99a04, Universite Aix-Marseille III.
    • Handle: RePEc:fth:aixmeq:99a04
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    22. Patacchini, Eleonora, 2003. "On the estimation of covariance matrices using panel data artificial regressions," Discussion Paper Series In Economics And Econometrics 0303, Economics Division, School of Social Sciences, University of Southampton.
    23. Felicitas Nowak-Lehmann D. & Inmaculada Martínez- Zarzoso, 2003. "The interplay of export supply and the real exchange rate. Evidence for Mercosur exports to the EU," International Trade 0309020, University Library of Munich, Germany.
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    More about this item

    Keywords

    REGRESSION ANALYSIS ; ESTIMATION OF PARAMETERS ; ECONOMETRIC MODELS;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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