Double Length Artificial Regressions

Artificial linear regressions often provide a convenient way to calculate test statistics and estimated covariance ma trices. This paper discusses one family of these regressions called d ouble length because the number of observations in the artificial reg ression is twice the actual number of observations. These double-leng th regressions can be useful in a wide variety of situations. They ar e quite easy to calculate, and, in contrast to the more widely applic able OPG regression, seem to have good properties when applied to sam ples of modest size. The authors first discuss how they are related t o the familiar Gauss-Newton and squared-residuals regressions for non linear regression models, then show how they may be used to test for functional form, and finally discuss several other ways in which they may be useful in applied econometric work. Copyright 1988 by Blackwell Publishing Ltd