Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/67825
Authors: 
Davidson, Russell
MacKinnon, James
Year of Publication: 
2001
Series/Report no.: 
Queen's Economics Department Working Paper 1038
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 artificial regression for regression models with heteroskedasticity of unknown form is introduced.
Subjects: 
artificial regression
LM test
specification test
Gauss-Newton regression
one-step estimation
OPG regression
double-length regression
binary response model
JEL: 
C12
C15
Document Type: 
Working Paper

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