A Score Test for Non-nested Hypotheses with Applications to Discrete Data Models
AbstractThis paper suggests that a convenient score test against non- nested alternatives can be constructed from the linear combination of the likelihood functions of the competing models. It is shown that this procedure is essentially a test for the correct specification of the conditional distribution of the variable of interest. As in Models for discrete data it is often necessary to fully specify the conditional distribution of the variate of interest, the test proposed here is particularly attractive in this context. The usefulness of the proposed tests is illustrated with applications to discrete choice and count data models.
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Bibliographic InfoPaper provided by University College London, Department of Economics in its series Discussion Papers with number 96-28 ISSN 1350-6722.
Length: 25 pages
Date of creation: Nov 1996
Date of revision:
Non-nested hypotheses; Score tests; Cox test; Linear mixtures.;
Other versions of this item:
- J. M. C. Santos Silva, 2001. "A score test for non-nested hypotheses with applications to discrete data models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 577-597.
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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