Chartered Financial Analyst Level 2

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Explanation:

The Durbin-Watson statistic is used to detect autocorrelation in the residuals from a regression analysis:

Durbin-Watson statistic close to 2.0: Indicates no autocorrelation in the residuals (Option A is correct)
Durbin-Watson statistic significantly less than 2.0: Indicates positive autocorrelation in the residuals
Durbin-Watson statistic significantly greater than 2.0: Indicates negative autocorrelation in the residuals

Let's analyze each option:

Option A: Correct - A Durbin-Watson statistic close to 2.0 indicates that the errors are serially uncorrelated.

Option B: Incorrect - A statistically significantly low value indicates positive serial correlation, not negative.

Option C: Incorrect - A statistically significantly high value indicates negative serial correlation, not positive.

Therefore, Option A is the most accurate statement about the Durbin-Watson statistic and linear trend models.

Explanation:

The Durbin-Watson statistic is used to detect autocorrelation in the residuals from a regression analysis:

Durbin-Watson statistic close to 2.0: Indicates no autocorrelation in the residuals (Option A is correct)
Durbin-Watson statistic significantly less than 2.0: Indicates positive autocorrelation in the residuals
Durbin-Watson statistic significantly greater than 2.0: Indicates negative autocorrelation in the residuals

Let's analyze each option:

Option A: Correct - A Durbin-Watson statistic close to 2.0 indicates that the errors are serially uncorrelated.

Option B: Incorrect - A statistically significantly low value indicates positive serial correlation, not negative.

Option C: Incorrect - A statistically significantly high value indicates negative serial correlation, not positive.

Therefore, Option A is the most accurate statement about the Durbin-Watson statistic and linear trend models.

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3 Which of the following statements about a linear trend model and the Durbin–Watson statistic is most accurate?

Exam-Like

A Durbin–Watson statistic close to 2.0 indicates that the errors in a time-series model are serially uncorrelated.

A statistically significantly low value of the Durbin–Watson statistic indicates that the errors in a time-series model are negatively serially correlated.

A statistically significantly high value of the Durbin–Watson statistic indicates that the errors in a time-series model are positively serially correlated.