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Multicollinearity is a common problem in linear regression models when two or more regressors are highly correlated, which yields some serious problems for the ordinary least square estimates of the parameters as well as model validation and interpretation. In this paper, rst the problem of multicollinearity and its subsequent effects on the linear regression along with some important measures for detecting multicollinearity is reviewed, then the role of eigenvalues and eigenvectors in detecting multicollinearity are bolded. At the end a real data set is evaluated for which the tted linear regression models is investigated for multicollinearity diagnostics.
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참고문헌 (21)
참고문헌 신청
M.P. Allen / 1997 / Understanding Regression Analysis / Springer :176~180
D. Asteriou / 2007 / Applied Econometrics: A Modern Approach Using Eviews and Microfit / Palgrave Macmillan Pub.
D.A. Belsley / 1991 / Conditioning Diagnostics: Collinearity and Weak Data Regression / John Wiley & Sons
D.A. Belsley / 1980 / Regression Diagnostics: Identifying In uential Data and Sources of Collinearity / John Wiley & Sons
N. Billington / 1999 / The location of foreign direct investment: an empirical analysis / Applied Economics 31(1):65~76
D. Asteriou / 2007 / Applied Econometrics: A Modern Approach Using Eviews and Microfit / Palgrave Macmillan Pub.
D.A. Belsley / 1991 / Conditioning Diagnostics: Collinearity and Weak Data Regression / John Wiley & Sons
D.A. Belsley / 1980 / Regression Diagnostics: Identifying In uential Data and Sources of Collinearity / John Wiley & Sons
N. Billington / 1999 / The location of foreign direct investment: an empirical analysis / Applied Economics 31(1):65~76
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