| 1. |
Two events, A and B, are said to be mutually exclusive if: |
| A. | P(A | B) = 1 |
| B. | P(B | A) = 1 |
| C. | P(A and B) = 1 |
| D. | P(A and B) = 0 |
| Answer» D. P(A and B) = 0 | |
| 2. |
A Type I error occurs when we: |
| A. | reject a false null hypothesis |
| B. | reject a true null hypothesis |
| C. | do not reject a false null hypothesis |
| D. | do not reject a true null hypothesis |
| Answer» B. reject a true null hypothesis | |
| 3. |
What is the meaning of the term "heteroscedasticity"? |
| A. | The variance of the errors is not constant |
| B. | The variance of the dependent variable is not constant |
| C. | The errors are not linearly independent of one another |
| D. | The errors have non- zero mean |
| Answer» A. The variance of the errors is not constant | |
| 4. |
What would be then consequences for the OLS estimator if heteroscedasticity is present in a regression model but ignored? |
| A. | It will be ignored |
| B. | It will be inconsistent |
| C. | It will be inefficient |
| D. | All of a),c), b) will be true. |
| Answer» C. It will be inefficient | |
| 5. |
Which one of the following is NOT a plausible remedy for near multicollinearity? |
| A. | Use principal components analysis |
| B. | Drop one of the collinear variables |
| C. | Use a longer run of data |
| D. | Take logarithems of each of the variables |
| Answer» D. Take logarithems of each of the variables | |
| 6. |
What will be the properties of the OLS estimator in the presence of multicollinearity? |
| A. | It will be consistent unbiased and efficient |
| B. | It will be consistent and unbiased but not efficient |
| C. | It will be consistent but not unbiased |
| D. | It will not be consistent |
| Answer» A. It will be consistent unbiased and efficient | |
| 7. |
A sure way of removing multicollinearity from the model is to |
| A. | Work with panel data |
| B. | Drop variables that cause multicollinearity in the first place |
| C. | Transform the variables by first differencing them |
| D. | Obtaining additional sample data |
| Answer» B. Drop variables that cause multicollinearity in the first place | |
| 8. |
Autocorrelation is generally occurred in |
| A. | Cross-section data |
| B. | Time series data |
| C. | Pooled data |
| D. | None of the above |
| Answer» B. Time series data | |
| 9. |
The regression coefficient estimated in the presence of autocorrelation in the sample data are NOT |
| A. | Unbiased estimators |
| B. | Consistent estimators |
| C. | Efficient estimators |
| D. | Linear estimators |
| Answer» C. Efficient estimators | |
| 10. |
In the regression function y=α + βx +c |
| A. | x is the regressor |
| B. | Y is the regressor |
| C. | x is the regressand |
| D. | none of these |
| Answer» A. x is the regressor | |
| 11. |
The coefficient of determination, r2 shows |
| A. | Proportion of the variation in the dependent variable Y is explained by the independent variable X |
| B. | Proportion of the variation in the dependent variable X is explained by the independent variable Y |
| C. | Proportion of the variation in ui is explained by the independent variable X |
| D. | Both a and c |
| Answer» A. Proportion of the variation in the dependent variable Y is explained by the independent variable X | |
| 12. |
BLUE is |
| A. | Best Linear Unbiased Estimator |
| B. | Best Linear Unconditional Estimator |
| C. | Basic Linear Unconditional Estimator |
| D. | Both b and c |
| Answer» A. Best Linear Unbiased Estimator | |
| 13. |
Data on one or variables collected at a given point of time |
| A. | Panel Data |
| B. | Time series data |
| C. | Pooled data |
| D. | Cross-section data |
| Answer» D. Cross-section data | |
| 14. |
The violation of the assumption of constant variance of the residual is known as |
| A. | Heteroscedasticity |
| B. | Multicollinearity |
| C. | Homoscedasticity |
| D. | Autocorrelation |
| Answer» A. Heteroscedasticity | |
| 15. |
Formula of coefficient determination is |
| A. | 1+RSS/TSS |
| B. | 1-RSS/ESS |
| C. | 1-RSS/TSS |
| D. | 1*RSS/TSS |
| Answer» C. 1-RSS/TSS | |
| 16. |
In confidence interval estimation, α = 5%, this means that this interval includes the true β with probability of |
| A. | 0.50% |
| B. | 50% |
| C. | 5% |
| D. | 95% |
| Answer» D. 95% | |
| 17. |
Consider a large population with a mean of 160 and a standard deviation of 25. A random sample of size 64 is taken from this population. What is the standard deviation of the sample mean? |
| A. | 3.125 |
| B. | 2.5 |
| C. | 3.75 |
| D. | 5.625 |
| Answer» A. 3.125 | |
| 18. |
In the case of multicollinearity which test will be insignificant? |
| A. | f test |
| B. | t test |
| C. | both a and b |
| D. | both are significant |
| Answer» B. t test | |
| 19. |
Hetroscedasticity is generally occurred in |
| A. | Cross-section data |
| B. | Time series data |
| C. | Pooled data |
| D. | None of the above |
| Answer» A. Cross-section data | |
| 20. |
When there are both qualitative and quantitative variables are there in the model, |
| A. | ANOVA |
| B. | ANCOVA |
| C. | CHI SQUARE |
| D. | All of the above |
| Answer» B. ANCOVA | |
| 21. |
When is the problem of dummy variable trap occur? |
| A. | When we take dummy variables more than the categories |
| B. | When we take dummy variables less than the categories |
| C. | When we take dummy variables equal to the no of categories |
| D. | Both a and c |
| Answer» D. Both a and c | |
| 22. |
Durbin Watson test is associated with: |
| A. | Heteroscedasticity |
| B. | Multicollinearity |
| C. | Autocorrelation |
| D. | Both a and c |
| Answer» C. Autocorrelation | |
| 23. |
All are the types of specification errors EXCEPT: |
| A. | Omission of relevant variable |
| B. | Inclusion of unnecessary variable |
| C. | errors of measurement |
| D. | over identified |
| Answer» D. over identified | |
| 24. |
White's test is used for the detection of ---------- -? |
| A. | multicollinearity |
| B. | hetroscedasticity |
| C. | Autocorrelation |
| D. | None of the above |
| Answer» B. hetroscedasticity | |
| 25. |
Which one is not the assumption of OLS? |
| A. | Perfect Multicollinearity |
| B. | zero covariance between error terms |
| C. | equal variance of disturbances |
| D. | Mean value of disturbances is |
| Answer» A. Perfect Multicollinearity | |
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