Abstract
The purpose of this paper is to examine the properties of various tests of linear and logarithmic (or log-linear) regression models. The test procedures may be categorized as follows: (1) tests that exploit the fact that the two models are intrinsically non-nested; (2) tests based on the Box-Cox data transformation; and (3) diagnostic tests of functional form misspecification against an unspecified alternative. The small-sample properties of several tests are investigated through a Monte Carlo experiment, as is their robustness to non-normality of the errors. Copyright 1988 by MIT Press.
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