Artificial regressions

Arbeitspapier

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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Queen's Economics Department Working Paper ; No. 1038

Classification and Topics

• Classification: Wirtschaft ; Hypothesis Testing: General ; Statistical Simulation Methods: General
• Subject: artificial regression ; LM test ; specification test ; Gauss-Newton regression ; one-step estimation ; OPG regression ; double-length regression ; binary response model

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Davidson, Russell ; MacKinnon, James
• Event: Veröffentlichung
• (who): Queen's University, Department of Economics
• (where): Kingston (Ontario)
• (when): 2001

Further information

• Handle: http://hdl.handle.net/10419/67825
• Last update: 10.03.2025, 11:43 AM CET