Model Specification Tests Based on Artificial Linear Regressions
提出一种通用方法，通过运行人工线性回归并进行常规显著性检验，来执行多种模型设定检验，包括非嵌套假设检验，例如检验线性回归模型与对数线性模型。
In this paper we develop an extremely general procedure for performing a wide variety of model specification tests by running artificial linear regressions and then using conventional significance tests. In particular, this procedure allows us to develop non-nested hypothesis tests for any set of models which attempt to explain the same dependent variable(s), even when the error specifications of the various models are not the same. For example, it is straightforward to test linear regression models against loglinear ones. These procedures are illustrated by an empirical application, in which we estimate and test several competing models of personal savings behavior in Canada.