The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Artificial Regressions

  • James G. MacKinnon
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2245

Abstract

An artificial regression is a linear regression that is associated with some other econometric model, which is usually nonlinear. It can be used for a variety of purposes, in particular computing covariance matrices and calculating test statistics. The best-known artificial regression is the GaussNewton regression, whose key properties are shared by all artificial regressions. The chief advantage of artificial regressions is conceptual: because econometricians are very familiar with linear regression models, using them for computation reduces the chance of errors and makes the results easier to comprehend intuitively.

Keywords

Artificial regressions Binary response model regression Bootstrap Double-length artificial regression Efficient score tests Gauss–Newton regression Generalized method of moments Heteroskedasticity Heteroskedasticity-consistent covariance matrices Instrumental variables Lagrange multiplier tests Multivariate nonlinear regression models Non-nested hypotheses Outer product of the gradient regression RESET test Score tests Specification 

JEL Classifications

C1 
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Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • James G. MacKinnon
    • 1
  1. 1.