Artificial Regressions
نویسندگان
چکیده
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. A slightly earlier version of this paper appeared as GREQAM Document de Travail 99a04. This research was supported, in part, by the Social Sciences and Humanities Research Council of Canada.
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