Câu hỏi: What result is proved by the Gauss-Markov theorem?

A. That OLS gives unbiased coefficient estimates

B. That OLS gives minimum variance coefficient estimates

C. That OLS gives minimum variance coefficient estimates only among the class of linear unbiased estimators

D. That OLS ensures that the errors are distributed normally

Câu 1:  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 2: 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 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: Consider an increase in the size of the test used to examine a hypothesis from 5% to 10%. Which one of the following would be an implication?

A. The probability of a Type I error is increased

B. The probability of a Type II error is increased

C. The rejection criterion has become more strict

D. The null hypothesis will be rejected less often

Câu 5: Suppose that we wanted to sum the 2007 returns on ten shares to calculate the return on a portfolio over that year. What method of calculating the individual stock returns would enable us to do this?

A. Simple

B. Continuously compounded

C. Neither approach would allow us to do this validly

D. Either approach could be used and they would both give the same portfolio return

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