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4 obtain additional or new data since

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4. Obtain additional or new data. Since multicollinearity is a sample feature, it is possible that in another sample involving the same variables collinearity may not be so serious as in the first sample. Sometimes simply increasing the size of the sample (if possible) may attenuate the collinearity problem. An often suggested remedy for multicollinearity is simply increasing the size of the sample, when possible. This remedy make sense from the perspective that increasing the sample size will improve the precision of OLS estimators; thus, reducing the adverse effects of multicollinearity. 5. Transform the variables. For example one could estimate a per capita version of the equation. You can use “ changes instead of “ levels . The advantage is that “ changes may not be as highly correlated as their levels . 6. Other methods of remedying multicollinearity. Multivariate statistical techniques such as factor analysis and principal components or techniques such as ridge regression are also employed to “solve” the problem of multicollinearity.
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ECON 301 - Introduction to Econometrics I May 2013 METU - Department of Economics Instructor: Dr. Ozan ERUYGUR e-mail: [email protected] Lecture Notes 18 5. Summary The effects of multicollinearity and the means of identifying them are summarized below: Estimates of parameters are still unbiased, consistent, and BLUE. The standard errors of the coefficients might be high, making individual coefficients insignificant. The model might exhibit high R 2 but low t -values: A joint F-test might reject the null hypothesis that several coefficients are zero, but individual t -tests might accept them, leading to an apparent contradiction. Coefficients may change considerably when variables are added or deleted, thus making the interpretation of individual coefficients difficult.
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