Lecture 24 2010 (1)

Lecture 24 2010 (1) - VI. Problems A. Multicollinearity B....

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1 VI. Problems A. Multicollinearity B. Heteroskedasticity C. Autocorrelation For each “Problem” you should be able to answer the following questions: What’s the problem? Define and Explain. What are the consequences of the problem? How do we diagnose the problem? How can we fix it? VI. Problems A. Multicollinearity 1. Definition : The presence of linear association among independent variables. 2. Consequences: • OLS estimators – remain unbiased. • Standard errors inflated t calc deflated. • Can’t trust your hypothesis tests. P(Type II Error) is high . b. Correlation Coefficients –how strong are the pair wise correlations between the Xs? 3. Diagnosis (Multicollinearity) How can we tell if we have this problem? a. Classic Signs: c. Auxilliary Regressions –regress one X on all the others. Is the R2 greater than 0.90? d. Variance Inflation Factors (VIF). Same information as R 2 from the Auxilliary Regs.
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2 4. Solutions – fixing the problem Sample data problem: Eliminate the offensive variable: Linear Association – try a non linear model Linear Association try a non linear model.
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This note was uploaded on 12/08/2011 for the course ECON 312 taught by Professor Daniellass during the Winter '10 term at UMass (Amherst).

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Lecture 24 2010 (1) - VI. Problems A. Multicollinearity B....

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