Which of the following is NOT a good reason for including lagged variables in a regression?

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. 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. 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. 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. 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

A. Continuous

B. Cardinal

C. Ordinal

D. Nominal

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