Welcome to the new version of CaltechAUTHORS. Login is currently restricted to library staff. If you notice any issues, please email coda@library.caltech.edu
Published September 15, 2017 | Submitted
Report Open

Selecting the Best Linear Regression Model: A Classical Approach


In this paper, we apply the model selection approach based on Likelihood Ratio (LR) tests developed in Vuong (1985) to the problem of choosing between two normal linear regression models which are not nested in each other. First we compare our model selection procedure to other model selection criteria. Then we explicitly derive the procedure when the competing linear models are non-nested and neither one is correctly specified. Some simplifications are seen to arise when both models are contained in a larger correctly specified linear regression model, or when at least one competing linear model is correctly specified. A comparison of our model selection tests and previous non-nested hypothesis tests concludes the paper.

Additional Information

This research was partially supported by National Science Foundation Grant SES-8410593. A preliminary draft of this paper was presented at the Southern California Econometric Conference at Lake Arrowhead, 1986. We are grateful to A. Golberger for helpful remarks and to D. Rivers for expected comments. The second author also thanks S. Heart for stimulating thoughts. Published as Lien, Donald, and Quang H. Vuong. "Selecting the best linear regression model: A classical approach." Journal of Econometrics 35.1 (1987): 3-23.

Attached Files

Submitted - sswp606.pdf


Files (1.6 MB)
Name Size Download all
1.6 MB Preview Download

Additional details

August 19, 2023
October 17, 2023