Published March 1986
| Version Submitted
Discussion Paper
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Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses
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Abstract
In this paper, we propose a classical approach to model selection. Using the Kullback-Leibler Information measure, we propose simple and directional likelihood-ratio tests for discriminating and choosing between two competing models whether the models are nonnested, overlapping or nested and whether both, one, or neither is misspecified. As a prerequisite, we fully characterize the asymptotic distribution of the likelihood ratio statistic under the most general conditions.
Additional Information
This research was supported by National Science Foundation Grant SES-8410593. I am indebted to P. Bjorn, D. Lien, and D. Rivers for helpful discussions, and to J. M. Dufour for some references on weighted sums of chi-square distributions. I would like to thank especially H. White whose comments much improved this paper. I am also grateful to C. R. Jackson without whom this paper would not have been written and to L. Donnelly for stimulating thoughts. Remaining errors are mine. Published as Vuong, Quang H. "Likelihood ratio tests for model selection and non-nested hypotheses." Econometrica: Journal of the Econometric Society (1989): 307-333.Attached Files
Submitted - sswp605.pdf
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sswp605.pdf
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- Eprint ID
- 81424
- Resolver ID
- CaltechAUTHORS:20170913-150620147
Related works
- Describes
- http://resolver.caltech.edu/CaltechAUTHORS:20171113-141635352 (URL)
Funding
- NSF
- SES-8410593
Dates
- Created
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2017-09-15Created from EPrint's datestamp field
- Updated
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2019-10-03Created from EPrint's last_modified field
Caltech Custom Metadata
- Caltech groups
- Social Science Working Papers
- Series Name
- Social Science Working Paper
- Series Volume or Issue Number
- 605