Model Specification Tests Based on Artificial Linear Regressions
AbstractThis paper develops a 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 models differ. For example, it is straightforward to test linear regression models against loglinear ones. These procedures are illustrated with an application to estimate competing models of personal savings in Canada.
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Bibliographic InfoPaper provided by Queen's University, Department of Economics in its series Working Papers with number 390.
Date of creation: 1980
Date of revision:
Other versions of this item:
- Davidson, Russell & MacKinnon, James G, 1984. "Model Specification Tests Based on Artificial Linear Regressions," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 25(2), pages 485-502, June.
- Russell Davidson & James G. MacKinnon, 1981. "Model Specification Tests Based on Artificial Linear Regressions," Working Papers 426, Queen's University, Department of Economics.
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