Violations of Classic Assumptions
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| Which classical assumption(s) does an Omitted Variable violate? | Violates Classical Assumptions I and III
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| What are the consequences of an Omitted Variable? | 1) Estimated coefficient doesn’t equal actual coefficient.
2) Bias is forced onto another coefficient, causing the estimated value of the coefficient to change.
3) Increases by decreasing variance.
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| What factors may indicate Omitted Variable bias? | 1) Unexpected signs on the coefficient.
2) Too big of coefficients (if positive bias on a positive coefficient).
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| How do you solve the problem of Omitted Variable bias? | Add omitted variable or proxy variable.
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| Which classical assumption(s) does a Redundant Variable violate? | Violates Classical Assumption VI
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| What are the consequences of a Redundant Variable? | 1) Does NOT introduce bias.
2) Increases variance.
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| How do you detect a Redundant Variable bias? | 1) Decreased R2.
2) Wald Test.
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| How do you solve the problem of Redundant Variable bias? | Drop irrelevant variable
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| How do you use to decide whether to include Omitted or Redundant Variables? | 1) Theory.
2) t-tests.
3) Adjusted R2 decreases if the improvement in overall fit due to addition of the independent variables to the regression does NOT outweigh loss in degrees of freedom.
4) Bias.
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| Which classical assumption(s) does omitting an Intercept violate? | Violates Classical Assumption II: the error term has a zero population mean.
Intercept usually absorbs error.
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| Which classical assumption(s) does Multicollinearity violate? | Violates Classical Assumption VI
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| What are the consequences of Multicollinearity? | 1) Does not cause bias
2) MIGHT CAUSE WRONG SIGN DUE TO INCREASED VARIANCE
3) Increases variance because it impacts r sub1,2
4) T-scores Fall because SE increases
5) Will not fall much
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| How do you detect Multicollinearity? | 1) High Adjusted R2 and low t-scores.
2) High simple correlation coefficients (r sub1,2).
3) High Variance Inflation Factor (VIF > 5).
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| How do you interpret VIF? | When VIF = 5, this means you have 5 times the variance in the model than you would have without the multicollinearity.
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| How do you solve the problem of Multicollinearity? | 1) Do nothing
2) Drop variables
3) Transform variables
4) Increase sample size
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| Which classical assumption(s) does Serial Correlation violate? | Violation of Classical Assumption IV
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| What are the consequences of Serial Correlation? | 1) No bias in coefficient estimates.
2) TRUE Increased variance in coefficient estimates.
3) Distorts SEE portion of SE.
4) OLS no longer BLUE.
5) OLS underestimates standard error of the coefficients.
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| How do you detect Serial Correlation? | 1) T-scores appear larger than they really are, leading to Type I error
More likely to make a Type I error (reject a null hypothesis that is true)
2) Durbin-Watson test
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| How do you solve the problem of Serial Correlation? | 1) Better Specification
2) Generalized Least Squares
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| Which classical assumption(s) does Heteroskedacity violate? | Violation of Classical Assumption V
Most common with cross-sectional models
Comparing proportionately different instances results in inconstant variance in error term
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| What are the consequences of Heteroskedacity? | 1) No bias in coefficient estimates, t-scores are larger.
2) TRUE Increased variance in coefficient estimates
3) Distorts SEE portion of SE
4) OLS is no longer BLUE
5) OLS underestimates standard error of the coefficients.
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| How do you detect Heteroskedacity? | 1) Plot residuals to see if variance is constant. If bell or flower shape emerges, there is heteroskedasticity.
2) Park Test (for proportionality)
3) White Test
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| How do you solve the problem of Heteroskedacity? | 1) Weighted Least Squares.
2) Redefinition of Variables.
3) Heteroskedasticity Corrected Standard Errors.
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