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

Continuous

Cardinal

Ordinal

Nominal

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

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

One minus the type II error

The confidence level

The size of the test

The size of the sample

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

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

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

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

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

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.

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

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

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

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

2.89

1.30

0.84

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

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

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 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

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

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