Published May 1987
| Version public
Journal Article
Selecting the best linear regression model: A classical approach
Creators
-
1.
University of Kansas
-
2.
California Institute of Technology
Abstract
In this paper, we apply the model selection approach based on likelihood ratio (LR) tests developed in Vuong (1986) to the problem of choosing between two normal linear regression models which are non-nested. We explicitly derive the procedure when the competing linear models are both misspecified. Some simplifications arise when the models are contained in a larger correctly specified linear regression model, or when one competing linear model is correctly specified.
Additional Information
© 1987, Elsevier Science Publishers B. V. (North-Holland). This research was partially supported by National Science Foundation Grant SES-8410593. We are grateful to participants at the Lake Arrowhead econometric conference and two referees for helpful remarks. The second author also thanks C.R. Jackson for stimulating thoughts and dedicates this paper to certain colleagues at Caltech. Formerly SSWP 606.Additional details
Identifiers
- Eprint ID
- 83158
- Resolver ID
- CaltechAUTHORS:20171113-140346394
Related works
- Describes
- http://resolver.caltech.edu/CaltechAUTHORS:20170913-142645530 (URL)
Funding
- NSF
- SES-8410593
Dates
- Created
-
2017-11-16Created from EPrint's datestamp field
- Updated
-
2021-11-15Created from EPrint's last_modified field