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Selecting the best linear regression model: A classical approach

Lien, Donald and Vuong, Quang H. (1987) Selecting the best linear regression model: A classical approach. Journal of Econometrics, 87 . pp. 3-23. ISSN 0304-4076. https://resolver.caltech.edu/CaltechAUTHORS:20171113-140346394

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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.


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https://doi.org/10.1016/0304-4076(87)90078-9DOIArticle
http://www.sciencedirect.com/science/article/pii/0304407687900789PublisherArticle
http://resolver.caltech.edu/CaltechAUTHORS:20170913-142645530Related ItemWorking Paper
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.
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NSFSES-8410593
Record Number:CaltechAUTHORS:20171113-140346394
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20171113-140346394
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:83158
Collection:CaltechAUTHORS
Deposited By: Jacquelyn Bussone
Deposited On:16 Nov 2017 00:26
Last Modified:03 Oct 2019 19:03

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