Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/189269 
Year of Publication: 
1999
Series/Report no.: 
Queen's Economics Department Working Paper No. 978
Publisher: 
Queen's University, Department of Economics, Kingston (Ontario)
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.
Subjects: 
Gauss-Newton Regression
Specification Test
Heteroskedasticity
JEL: 
C12
Document Type: 
Working Paper

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