Artificial Regressions

James MacKinnon and Russell Davidson

No 1038, 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 artificialregressions 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.

Keywords: artificial regression; LM test; specification test; Gauss-Newton regression; one-step estimation; OPG regression; double-length regression; binary response model (search for similar items in EconPapers)
JEL-codes: C12 C15 (search for similar items in EconPapers)
Pages: 24 pages
Date: 2001-01
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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https://www.econ.queensu.ca/sites/econ.queensu.ca/files/qed_wp_1038.pdf First version 2001 (application/pdf)

Related works:
Working Paper: Artificial Regressions (1999)
Working Paper: Artificial Regressions (1999)
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Persistent link: https://EconPapers.repec.org/RePEc:qed:wpaper:1038

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