Even if heteroscedaticity is suspected and detected, it is not easy to correct the problem. This statement is Select one: True Depends on test statistics Sometimes true False
Even if heteroscedaticity is suspected and detected, it is not easy to correct the problem. This statement is
Select one:
- True
- Depends on test statistics
- Sometimes true
- False
if the residuals from a regression estimated using a small sample of data are not
Select one:
- The coefficient estimate will be biased inconsistent
- The coefficient estimate will be biased consistent
- Test statistics concerning the parameter will not follow their assumed distributions
- The coefficient estimate will be unbiased inconsistent
males vs female
young vs adult
married vs unmarried
urban resident vs suburban
good credit score vs poor credit score
The coefficient of determination, r squared shows
Select one:
- Proportion on the variation in the dependent variable error term is explained by the independent variable X
- Proportion of the variation in the dependent variable X is explained by the error term
- Proportion of the variation in the dependent variable Y is explained by the independent variable X
- Proportion of the variation in the dependent variable X is explained by the independent variable Y
If multicollinearity is perfect in a regression model then the regression coefficients of the explanatory variables are
Select one:
- Indeterminate
- Infinite values
- Determinate
- Small negative values
Multicollinearity is limited to
Select one:
- Cross-section data
- Pooled data
- cross-section data, time series data and pooled data
- Time series data
If there exist high multicollinearity, then the regression coefficients are
Select one:
- Indeterminate
- Infinite values
- Determinate
- small negative values
What will be the properties of the OLS estimator in the presence of multicollinearity?
Select one:
- it will not be consistent
- it will be consistent unbiased and efficient
- it will be consistent and unbiased but not efficient
- it will be consistent but not unbiased
Two distributed lag model;
Answer:
In a regression model with multicollinearity being very high, the estimators
Select one:
- are unbiased
- are consistent
- Standard errors are correctly estimated
- Biased
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