150 câu Trắc nghiệm tổng hợp Kinh tế lượng có đáp án (Phần 4)

A. Continuous

B. Cardinal

C. Ordinal

D. Nominal

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

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

A. One minus the type II error

B. The confidence level

C. The size of the test

D. The size of the sample

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

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

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

A. p-values can only be used for two-sided tests

B. It is the marginal significance level where we would be indifferent between rejecting and not rejecting the null hypothesis

C. It is the exact significance level for the test

D. Given the p-value, we can make inferences without referring to statistical tables

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

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.

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

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

A. A t-distribution with zero degrees of freedom is a normal

B. A t-distribution with one degree of freedom is a normal

C. A t-distribution with infinite degrees of freedom is a normal

D. There is no relationship between the two distributions

A. The standard normal is a special case of the t-distribution, the square of which is a special case of the F-distribution

B. Since the three distributions are related, the 5% critical values from each will be the same

C. Asymptotically, a given test conducted using any of the three distributions will lead to the same conclusion

D. The normal and t- distributions are symmetric about zero while the F- takes only positive values

A. 2.89

B. 1.30

C. 0.84

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

A. A t-variate with z degrees of freedom is also an F(1, z)

B. The square of a t-variate with z degrees of freedom is also an F(1, z)

C. A t-variate with z degrees of freedom is also an F(z, 1)

D. There is no relationship between the two distributions

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

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

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

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