Câu hỏi:  Which of the following is NOT a good reason for including lagged variables in a regression?

A. Slow response of the dependent variable to changes in the independent variables

B. Over-reactions of the dependent variables

C. The dependent variable is a centred moving average of the past 4 values of the series

D. The residuals of the model appear to be non-normal

Câu 1: 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 2: Which of the following is a correct interpretation of a “95% confidence interval” for a regression parameter?

A. We are 95% sure that the interval contains the true value of the parameter

B. We are 95% sure that our estimate of the coefficient is correct

C. We are 95% sure that the interval contains our estimate of the coefficient

D. In repeated samples, we would derive the same estimate for the coefficient 95% of the time

Câu 3: 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 4: Which of the following is the most accurate definition of the term “the OLS estimator”?

A. It comprises the numerical values obtained from OLS estimation

B. It is a formula that, when applied to the data, will yield the parameter estimates

C. It is equivalent to the term “the OLS estimate”

D. It is a collection of all of the data used to estimate a linear regression model.

Câu 5: The type I error associated with testing a hypothesis is equal to:

A. One minus the type II error

B. The confidence level

C. The size of the test

D. The size of the sample

Câu 6: 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