Testing Separate Regression Models Subject to Specification Error
AbstractWithin the framework of linear regression, errors arising from artificial inclusion or exclusion of variables are considered with augmentations or restrictions on a given maintained hypothesis. This permits exploitation of relations between tests based on Wald and Lagrange Multiplier Principles. It is demonstrated that the standard F test, though based on biased estimators, is nevertheless valid. The traditional analysis of misspecification is applied to the linear specialization of tests for separate families of hypotheses. An empirical example is provided examining the effect of labour legislation on the growth of Canadian trade union membership, using annual data for 1925-72.
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Bibliographic InfoPaper provided by Queen's University, Department of Economics in its series Working Papers with number 441.
Date of creation: 1981
Date of revision:
Other versions of this item:
- McAleer, Michael & Fisher, Gordon, 1982. "Testing separate regression models subject to specification error," Journal of Econometrics, Elsevier, vol. 19(1), pages 125-145, May.
- Mcaleer, M. & Fisher, G., 1982. "Testing Separate Regression Models Subject to Specification Error," Cahiers de recherche 8216, Universite de Montreal, Departement de sciences economiques.
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- McAleer, Michael, 1995. "The significance of testing empirical non-nested models," Journal of Econometrics, Elsevier, vol. 67(1), pages 149-171, May.
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