20 câu hỏi
The numerical score assigned to the credit rating of a bond is best described as what type of number?
Continuous
Cardinal
Ordinal
Nominal
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
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
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
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
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
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
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
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
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.
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
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
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
Consider a standard normally distributed variable, a t-distributed variable with d degrees of freedom, and an F-distributed variable with (1, d) degrees of freedom. Which of the following statements is FALSE?
The standard normal is a special case of the t-distribution, the square of which is a special case of the F-distribution
Since the three distributions are related, the 5% critical values from each will be the same
Asymptotically, a given test conducted using any of the three distributions will lead to the same conclusion
The normal and t- distributions are symmetric about zero while the F- takes only positive values
Which of the following is the correct value for?
2.89
1.30
0.84
We cannot determine the value of from the information given in the question
What is the relationship, if any, between t-distributed and F-distributed random variables?
A t-variate with z degrees of freedom is also an F(1, z)
The square of a t-variate with z degrees of freedom is also an F(1, z)
A t-variate with z degrees of freedom is also an F(z, 1)
There is no relationship between the two distributions
Which of the following would NOT be a potential remedy for the problem of multicollinearity between regressors?
Removing one of the explanatory variables
Transforming the data into logarithms
Transforming two of the explanatory variables into ratios
Collecting higher frequency data on all of the variables
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?
The residuals are positively autocorrelated
The residuals are negatively autocorrelated
There is no autocorrelation in the residuals
The test statistic has fallen in the intermediate region
Which of the following is NOT a good reason for including lagged variables in a regression?
Slow response of the dependent variable to changes in the independent variables
Over-reactions of the dependent variables
The dependent variable is a centred moving average of the past 4 values of the series
The residuals of the model appear to be non-normal
Which of the following would you expect to be a problem associated with adding lagged values of the dependent variable into a regression equation?
The assumption that the regressors are non-stochastic is violated
A model with many lags may lead to residual non-normality
Adding lags may induce multicollinearity with current values of variables
The standard errors of the coefficients will fall as a result of adding more explanatory variables