Lecture12 - Lecture 12 : Multicollinearity,...

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Unformatted text preview: Lecture 12 : Multicollinearity, Heteroskedasticity Econ 444, Winter 2010 Feb 23, 2010 How to detect Multicollinearity ? 1 High R 2 but few significant t-ratios may suggest presence of multicollinearity. 2 High pairwise correlations among explanatory variables. 3 Variance Inflation Factor (VIF) : measures how much multicollinearity has increased the variance of an estimated coefficient. For example, consider the following regression equation, Y i = + 1 X 1 i + 2 X 2 i + 3 X 3 i + i (1) Now to compute the VIF corresponding to say estimate of 1 we follow two steps: 1 Estimate the following equation and save the R 2 1 : X 1 = 1 + 2 X 2 + 3 X 3 + i (2) 2 Then the VIF for 1 is given by, VIF ( 1 ) = 1 1- R 2 1 (3) Similarly to obtain VIF corresponding to 2 , 1 Estimate the following equation and save the R 2 2 : X 2 = 1 + 2 X 1 + 3 X 3 + i (4) 2 Then the VIF for 1 is given by, VIF ( 2 ) = 1 1- R 2 2 (5) Few things to note : There will be a VIF for every coefficient estimated in the equation. So in previous example, there will be 3 VIFs....
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This note was uploaded on 04/13/2010 for the course ECON 444 taught by Professor Ogaki during the Winter '07 term at Ohio State.

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Lecture12 - Lecture 12 : Multicollinearity,...

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