Selecting the best linear regression model. A classical approach

Donald Lien; Quang H. Vuong

doi:10.1016/0304-4076(87)90078-9

Selecting the best linear regression model. A classical approach

Donald Lien, Quang H. Vuong

Research output: Contribution to journal › Article › peer-review

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 computing linear model is correctly specified.

Original language	English (US)
Pages (from-to)	3-23
Number of pages	21
Journal	Journal of Econometrics
Volume	35
Issue number	1
DOIs	https://doi.org/10.1016/0304-4076(87)90078-9
State	Published - May 1987

ASJC Scopus subject areas

Economics and Econometrics

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10.1016/0304-4076(87)90078-9

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title = "Selecting the best linear regression model. A classical approach",

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 computing linear model is correctly specified.",

author = "Donald Lien and Vuong, {Quang H.}",

note = "Funding Information: *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.",

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Selecting the best linear regression model. A classical approach

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