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 Gauss–Newton 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.
KeywordsArtificial 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
- Davidson, R., and J.G. MacKinnon. 2001. Artificial regressions. In Companion to theoretical econometrics, ed. B. Baltagi. Oxford: Blackwell.Google Scholar
- Davidson, R., and J.G. MacKinnon. 2004. Econometric theory and methods. New York: Oxford University Press.Google Scholar
- Davidson, R., and J.G. MacKinnon. 2006. Bootstrap methods in econometrics. In Palgrave handbooks of econometrics. volume 1: Econometric theory, ed. T.C. Mills and K.D. Patterson. Basingstoke: Palgrave Macmillan.Google Scholar
- MacKinnon, J.G. 1992. Model specification tests and artificial regressions. Journal of Economic Literature 30: 102–146.Google Scholar
- Orme, C.D. 1995. On the use of artificial regressions in certain microeconometric models. Econometrica 11: 290–305.Google Scholar
- Ramsey, J.B. 1969. Tests for specification errors in classical linear least-squares regression analysis. Journal of the Royal Statistical Society, Series B 31: 350–371.Google Scholar