Double-Length Artificial Regressions

Russell Davidson and James MacKinnon ()

Working Papers from Queen's University, Department of Economics

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.

Keywords: artificial regression; double-length regression; DLR; Gauss-Newton regression; functional form (search for similar items in EconPapers)
Date: Written
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Published in Oxford Bulletin of Economics and Statistics, 50, 1988

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http://www.econ.queensu.ca/working_papers/papers/qed_wp_691.pdf First version 1987 (application/pdf)

Related works:
Journal Article: Double Length Artificial Regressions (1988)
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Persistent link: http://EconPapers.repec.org/RePEc:qed:wpaper:691

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