Câu hỏi: 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 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 one of the following is NOT an assumption of the classical linear regression model?

A. The explanatory variables are uncorrelated with the error terms

B. The disturbance terms have zero mean

C. The dependent variable is not correlated with the disturbance terms

D. The disturbance terms are independent of one another

Câu 3: 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 4: The value of the Durbin Watson test statistic in a regression with 4 regressors (including the constant term) estimated on 100 observations is 3.6. What might we suggest from this? 

A. The residuals are positively autocorrelated

B. The residuals are negatively autocorrelated

C. There is no autocorrelation in the residuals

D. The test statistic has fallen in the intermediate region

Câu 5: 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 6: 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