Q1. An analyst is trying to estimate the beta for a fund. The analyst estimates a regression equation in which the fund returns are the dependent variable and the Wilshire 5000 is the independent variable, using monthly data over the past five years. The analyst finds that the correlation between the square of the residuals of the regression and the Wilshire 5000 is 0.2. Which of the following is most accurate, assuming a 0.05 level of significance? There is:
A) no evidence that there is conditional heteroskedasticity or serial correlation in the regression equation. B) evidence of serial correlation but not conditional heteroskedasticity in the regression equation. C) evidence of conditional heteroskedasticity but not serial correlation in the regression equation.
Q2. Which of the following is least likely a method used to detect heteroskedasticity? A) Test of the variances. B) Durbin-Watson test. C) Breusch-Pagan test.
Q3. Consider the following graph of residuals and the regression line from a time-series regression:
These residuals exhibit the regression problem of: A) autocorrelation. B) homoskedasticity. C) heteroskedasticity.
Q4. Which of the following statements regarding heteroskedasticity is FALSE? A) Multicollinearity is a potential problem only in multiple regressions, not simple regressions. B) Heteroskedasticity only occurs in cross-sectional regressions. C) The presence of heteroskedastic error terms results in a variance of the residuals that is too large.
Q5. Which of the following statements regarding heteroskedasticity is FALSE? A) Heteroskedasticity results in an estimated variance that is too large and, therefore, affects statistical inference. B) The assumption of linear regression is that the residuals are heteroskedastic. C) Heteroskedasticity may occur in cross-section or time-series analyses.
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发表于 2009-1-8 14:30
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