# 93 câu trắc nghiệm Kinh tế lượng – Phần 1

Tổng hợp 93 câu trắc nghiệm Kinh tế lượng cơ bản trong tài chính bằng tiếng anh (có đáp án kèm theo). Nội dung được phân thành 9 chương, được chia làm 2 phần. Các câu hỏi trắc nghiệm phần 1 bao gồm:

**KTL_002_C1_1**: The numerical score assigned to the credit rating of a bond is best described as what type of number?

○ Continuous

○ Cardinal

● Ordinal

○ Nominal

**KTL_002_C1_2**: 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?

● Simple

○ Continuously compounded

○ Neither approach would allow us to do this validly

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

**KTL_002_C1_3**: Consider a bivariate regression model with coefficient standard errors calculated using the usual formulae. Which of the following statements is/are correct regarding the standard error estimator for the slope coefficient?

(i) It varies positively with the square root of the residual variance (s)

(ii) It varies positively with the spread of X about its mean value

(iii) It varies positively with the spread of X about zero

(iv) It varies positively with the sample size T

● (i) only

○ (i) and (iv) only

○ (i), (ii) and (iv) only

○ (i), (ii), (iii) and (iv).

**KTL_002_C1_4**: In a time series regression of the excess return of a mutual fund on a constant and the excess return on a market index, which of the following statements should be true for the fund manager to be considered to have “beaten the market” in a statistical sense?

● The estimate for \(\alpha \) should be positive and statistically significant

○ The estimate for \(\alpha \) should be positive and statistically significantly greater than the risk-free rate of return

○ The estimate for \(\alpha \) should be positive and statistically significant

○ The estimate for \(\alpha \) should be negative and statistically significant.

**KTL_002_C1_5**: What result is proved by the Gauss-Markov theorem?

○ That OLS gives unbiased coefficient estimates

○ That OLS gives minimum variance coefficient estimates

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

○ That OLS ensures that the errors are distributed normally

**KTL_002_C1_6**: The type I error associated with testing a hypothesis is equal to

○ One minus the type II error

○ The confidence level

● The size of the test

○ The size of the sample

**KTL_002_C1_7**: Which of the following is a correct interpretation of a “95% confidence interval” for a regression parameter?

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

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

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

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

**KTL_002_C1_8**: Which of the following statements is correct concerning the conditions required for OLS to be a usable estimation technique?

● The model must be linear in the parameters

○ The model must be linear in the variables

○ The model must be linear in the variables and the parameters

○ The model must be linear in the residuals.

**KTL_002_C1_9**: Which of the following is NOT a good reason for including a disturbance term in a regression equation?

○ It captures omitted determinants of the dependent variable

● To allow for the non-zero mean of the dependent variable

○ To allow for errors in the measurement of the dependent variable

○ To allow for random influences on the dependent variable

**KTL_002_C1_10**: Which of the following is NOT correct with regard to the p-value attached to a test statistic?

● p-values can only be used for two-sided tests

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

○ It is the exact significance level for the test

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

**KTL_002_C1_11**: Which one of the following is NOT an assumption of the classical linear regression model?

○ The explanatory variables are uncorrelated with the error terms.

○ The disturbance terms have zero mean

● The dependent variable is not correlated with the disturbance terms

○ The disturbance terms are independent of one another.

**KTL_002_C1_12**: Which of the following is the most accurate definition of the term “the OLS estimator”?

○ It comprises the numerical values obtained from OLS estimation

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

○ It is equivalent to the term “the OLS estimate”

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

**KTL_002_C1_13**: 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?

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

● Researcher 2 will have a higher probability of type I error

○ Researcher 1 will be more likely to reject the null hypothesis

○ Both researchers will always reach the same conclusion.

**KTL_002_C1_14**: 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?

● The probability of a Type I error is increased

○ The probability of a Type II error is increased

○ The rejection criterion has become more strict

○ The null hypothesis will be rejected less often.

**KTL_002_C1_15**: What is the relationship, if any, between the normal and t-distributions?

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

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

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

○ There is no relationship between the two distributions.