Câu hỏi: Which of the following would NOT be a potential remedy for the problem of multicollinearity between regressors?

A. Removing one of the explanatory variables

B. Transforming the data into logarithms

C. Transforming two of the explanatory variables into ratios

D. Collecting higher frequency data on all of the variables

Câu 1: Which of the following is NOT a good reason for including a disturbance term in a regression equation?

A. It captures omitted determinants of the dependent variable

B. To allow for the non-zero mean of the dependent variable

C. To allow for errors in the measurement of the dependent variable

D. To allow for random influences on the dependent variable

Câu 2: Which of the following is the correct value for?

A. 2.89

B. 1.30

C. 0.84

D. We cannot determine the value of from the information given in the question

Câu 3: Which of the following would you expect to be a problem associated with adding lagged values of the dependent variable into a regression equation?

A. The assumption that the regressors are non-stochastic is violated

B. A model with many lags may lead to residual non-normality

C. Adding lags may induce multicollinearity with current values of variables

D. The standard errors of the coefficients will fall as a result of adding more explanatory variables

Câu 4: Two researchers have identical models, data, coefficients and standard error estimates. They test the same hypothesis using a two-sided alternative, but researcher 1 uses a 5% size of test while researcher 2 uses a 10% test. Which one of the following statements is correct?

A. Researcher 2 will use a larger critical value from the t-tables

B. Researcher 2 will have a higher probability of type I error

C. Researcher 1 will be more likely to reject the null hypothesis

D. Both researchers will always reach the same conclusion

Câu 5: The numerical score assigned to the credit rating of a bond is best described as what type of number?

A. Continuous

B. Cardinal

C. Ordinal

D. Nominal

Câu 6: Which of the following statements is correct concerning the conditions required for OLS to be a usable estimation technique?

A. The model must be linear in the parameters

B. The model must be linear in the variables

C. The model must be linear in the variables and the parameters

D. The model must be linear in the residuals