Artificial Regressions

James MacKinnon and Russell Davidson

No 978, Working Paper from Economics Department, Queen's University

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.

Keywords: Heteroskedasticity; Gauss-Newton Regression; Specification Test (search for similar items in EconPapers)
JEL-codes: C12 (search for similar items in EconPapers)
Pages: 23 pages
Date: 1999-01
References: Add references at CitEc
Citations: View citations in EconPapers (2)

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https://www.econ.queensu.ca/sites/econ.queensu.ca/files/qed_wp_978.pdf First version 1999 (application/pdf)

Related works:
Working Paper: Artificial Regressions (2001)
Working Paper: Artificial Regressions (1999)
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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:978

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