Reading 12- LOS g(Part 2): Q6-8 style, black, regarding, following, statements

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6．Which of the following statements regarding serial correlation that might be encountered in regression analysis is least accurate?
A)    Positive serial correlation typically has the same effect as heteroskedasticity.
B)    Negative serial correlation causes a failure to reject the null hypothesis when it is actually false.
C)    Positive serial correlation is much more common in economic and financial data than negative serial correlation.
D)    Serial correlation occurs least often with time series data.

7．Alex Wade, CFA, is analyzing the result of a regression analysis comparing the performance of gold stocks versus a broad equity market index. Wade believes that serial correlation may be present, and in order to prove his theory, should use which of the following methods to detect its presence?
A)    The Breusch-Pagan test.
B)    The Hansen method.
C)    The Durbin-Watson statistic.
D)    The use of White-corrected standard errors.

8．During the course of a multiple regression analysis, an analyst has observed several items that she believes may render incorrect conclusions. For example, the coefficient standard errors are too small, although the estimated coefficients are accurate. She believes that these small standard error terms will result in the computed t-statistics being too big, resulting in too many Type I errors. The analyst has most likely observed which of the following assumption violations in her regression analysis?
A)    Positive serial correlation.
B)    Negative serial correlation.
C)    Heteroskedasticity.
D)    Multicollinearity.

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6．Which of the following statements regarding serial correlation that might be encountered in regression analysis is least accurate?
A)    Positive serial correlation typically has the same effect as heteroskedasticity.
B)    Negative serial correlation causes a failure to reject the null hypothesis when it is actually false.
C)    Positive serial correlation is much more common in economic and financial data than negative serial correlation.
D)    Serial correlation occurs least often with time series data.
The correct answer was D)
Serial correlation, which is sometimes referred to as autocorrelation, occurs when the residual terms are correlated with one another, and is most frequently encountered with time series data.

7．Alex Wade, CFA, is analyzing the result of a regression analysis comparing the performance of gold stocks versus a broad equity market index. Wade believes that serial correlation may be present, and in order to prove his theory, should use which of the following methods to detect its presence?
A)    The Breusch-Pagan test.
B)    The Hansen method.
C)    The Durbin-Watson statistic.
D)    The use of White-corrected standard errors.
The correct answer was C)
The Durbin-Watson statistic is the most commonly used method for the detection of serial correlation, although residual plots can also be utilized. For a large sample size, DW ≈ 2(1-r), where r is the correlation coefficient between residuals from one period and those from a previous period. The DW statistic is then compared to a table of DW statistics that gives upper and lower critical values for various sample sizes, levels of significance and numbers of degrees of freedom to detect the presence or absence of serial correlation.

8．During the course of a multiple regression analysis, an analyst has observed several items that she believes may render incorrect conclusions. For example, the coefficient standard errors are too small, although the estimated coefficients are accurate. She believes that these small standard error terms will result in the computed t-statistics being too big, resulting in too many Type I errors. The analyst has most likely observed which of the following assumption violations in her regression analysis?
A)    Positive serial correlation.
B)    Negative serial correlation.
C)    Heteroskedasticity.
D)    Multicollinearity.
The correct answer was A)
Positive serial correlation is the condition where a positive regression error in one time period increases the likelihood of having a positive regression error in the next time period. The residual terms are correlated with one another, leading to coefficient error terms that are too small.