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Selecting the Best Linear Regression Model: A Classical Approach

Lien, Donald and Vuong, Quang H. (1986) Selecting the Best Linear Regression Model: A Classical Approach. Social Science Working Paper, 606. California Institute of Technology , Pasadena, CA. (Unpublished)

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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.

Item Type:Report or Paper (Discussion Paper)
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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.
Group:Social Science Working Papers
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Series Name:Social Science Working Paper
Issue or Number:606
Record Number:CaltechAUTHORS:20170913-142645530
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:81421
Deposited By: Jacquelyn Bussone
Deposited On:15 Sep 2017 18:11
Last Modified:03 Oct 2019 18:42

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