Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/189089 
Year of Publication: 
1987
Series/Report no.: 
Queen's Economics Department Working Paper No. 691
Publisher: 
Queen's University, Department of Economics, Kingston (Ontario)
Abstract: 
Artificial linear regressions often provide a convenient way to calculate test statistics and estimate covariance matrices. This paper discusses one family of these regressions, called "double-length" because the number of observations in the artificial regression is twice the actual number of observations. These double-length regressions can be useful in a wide variety of situations. They are easy to calculate, and seem to have good properties when applied to samples of modest size. We first discuss how they are related to Gauss-Newton and squared-residuals regressions for nonlinear models, and then show how they may be used to test for functional form and other applications.
Subjects: 
artificial regression
double-length regression
DLR
Gauss-Newton regression
functional form
Document Type: 
Working Paper

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