答案和详解如下： Q9. Suppose there is evidence that the variance of the error term is correlated with the values of the independent variables. The most likely effect on the statistical inferences Smith can make from the regressions results is to commit a: A) Type II error by incorrectly failing to reject the null hypothesis that the regression parameters are equal to zero. B) Type I error by incorrectly rejecting the null hypotheses that the regression parameters are equal to zero. C) Type I error by incorrectly failing to reject the null hypothesis that the regression parameters are equal to zero. Correct answer is B) One problem with heteroskedasticity is that the standard errors of the parameter estimates will be too small and the t-statistics too large. This will lead Smith to incorrectly reject the null hypothesis that the parameters are equal to zero. In other words, Smith will incorrectly conclude that the parameters are statistically significant when in fact they are not. This is an example of a Type I error: incorrectly rejecting the null hypothesis when it should not be rejected. Q10. Which of the following is most likely to indicate that two or more of the independent variables, or linear combinations of independent variables, may be highly correlated with each other? Unless otherwise noted, significant and insignificant mean significantly different from zero and not significantly different from zero, respectively.
A) The R2 is low, the F-statistic is insignificant and the Durbin-Watson statistic is significant. B) The R2 is high, the F-statistic is significant and the t-statistics on the individual slope coefficients are insignificant. C) The R2 is high, the F-statistic is significant and the t-statistics on the individual slope coefficients are significant. Correct answer is B) Multicollinearity occurs when two or more of the independent variables, or linear combinations of independent variables, may be highly correlated with each other. In a classic effect of multicollinearity, the R2 is high and the F-statistic is significant, but the t-statistics on the individual slope coefficients are insignificant. Q11. Suppose there is evidence that two or more of the independent variables, or linear combinations of independent variables, may be highly correlated with each other. The most likely effect on the statistical inferences Smith can make from the regression results is to commit a: A) Type I error by incorrectly rejecting the null hypothesis that the regression parameters are equal to zero. B) Type I error by incorrectly failing to reject the null hypothesis that the regression parameters are equal to zero.
C) Type II error by incorrectly failing to reject the null hypothesis that the regression parameters are equal to zero. Correct answer is C) One problem with multicollinearity is that the standard errors of the parameter estimates will be too large and the t-statistics too small. This will lead Smith to incorrectly fail to reject the null hypothesis that the parameters are statistically insignificant. In other words, Smith will incorrectly conclude that the parameters are not statistically significant when in fact they are. This is an example of a Type II error: incorrectly failing to reject the null hypothesis when it should be rejected. Q12. Using the Durban-Watson test statistic, Smith rejects the null hypothesis suggested by the test. This is evidence that:
A) two or more of the independent variables are highly correlated with each other. B) the error terms are correlated with each other. C) the error term is normally distributed. Correct answer is B) Serial correlation (also called autocorrelation) exists when the error terms are correlated with each other. Multicollinearity, on the other hand, occurs when two or more of the independent variables are highly correlated with each other. One assumption of multiple regression is that the error term is normally distributed. Q13. An analyst is estimating whether a fund’s excess return for a quarter is related to interest rates and last quarter’s excess return. The regression equation is found to have unconditional heteroskedasticity and serial correlation. Which of the following is most accurate? Parameter estimates will be:
A) inaccurate and statistical inference about the parameters will not be valid. B) accurate but statistical inference about the parameters will not be valid. C) inaccurate but statistical inference about the parameters will be valid. Correct answer is A) One of the independent variables is a lagged value of the dependent variable. This means that serial correlation will cause an inaccurate parameter estimate. Serial correlation always impacts the statistical inference about the parameters. Unconditional heteroskedasticity never impacts statistical inference or parameter accuracy. | 
发表于 2009-1-8 14:33
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