Clara Holmes, CFA, is attempting to model the importation of an herbal tea into the United States. She gathers 24 years of annual data, which is in millions of inflation-adjusted dollars. The real dollar value of the tea imports has grown steadily from $30 million in the first year of the sample to $54 million in the most recent year.

She computes the following equation:

(Tea Imports)t = 3.8836 + 0.9288 × (Tea Imports)t ? 1 + et

t-statistics (0.9328)(9.0025)

R2 = 0.7942

Adj. R2 = 0.7844

SE = 3.0892

N = 23

Holmes and her colleague, John Briars, CFA, discuss the implication of the model and how they might improve it. Holmes is fairly satisfied with the results because, as she says “the model explains 78.44 percent of the variation in the dependent variable.” Briars says the model actually explains more than that.

Briars asks about the Durbin-Watson statistic. Holmes said that she did not compute it, so Briars reruns the model and computes its value to be 2.1073. Briars says “now we know serial correlation is not a problem.” Holmes counters by saying “rerunning the model and computing the Durbin-Watson statistic was unnecessary because serial correlation is never a problem in this type of time-series model.”

Briars and Holmes decide to ask their company’s statistician about the consequences of serial correlation. Based on what Briars and Holmes tell the statistician, the statistician informs them that serial correlation will only affect the standard errors and the coefficients are still unbiased. The statistician suggests that they employ the Hansen method, which corrects the standard errors for both serial correlation and heteroskedasticity.

Given the information from the statistician, Briars and Holmes decide to use the estimated coefficients to make some inferences. Holmes says the results do not look good for the future of tea imports because the coefficient on (Tea Import)t ? 1 is less than one. This means the process is mean reverting. Using the coefficients in the output, says Holmes, “we know that whenever tea imports are higher than 41.810, the next year they will tend to fall. Whenever the tea imports are less than 41.810, then they will tend to rise in the following year.” Briars agrees with the general assertion that the results suggest that imports will not grow in the long run and tend to revert to a long-run mean, but he says the actual long-run mean is 54.545. Briars then computes the forecast of imports three years into the future.

Part 1)

With respect to the statements made by Holmes and Briars concerning serial correlation and the importance of the Durbin-Watson statistic:

A) Holmes was correct and Briars was incorrect.

B) Briars was correct and Holmes was incorrect.

C) they were both correct.

D) they were both incorrect.

Clara Holmes, CFA, is attempting to model the importation of an herbal tea into the United States. She gathers 24 years of annual data, which is in millions of inflation-adjusted dollars. The real dollar value of the tea imports has grown steadily from $30 million in the first year of the sample to $54 million in the most recent year.

She computes the following equation:

(Tea Imports)t = 3.8836 + 0.9288 × (Tea Imports)t ? 1 + et

t-statistics (0.9328)(9.0025)

R2 = 0.7942

Adj. R2 = 0.7844

SE = 3.0892

N = 23

Holmes and her colleague, John Briars, CFA, discuss the implication of the model and how they might improve it. Holmes is fairly satisfied with the results because, as she says “the model explains 78.44 percent of the variation in the dependent variable.” Briars says the model actually explains more than that.

Briars asks about the Durbin-Watson statistic. Holmes said that she did not compute it, so Briars reruns the model and computes its value to be 2.1073. Briars says “now we know serial correlation is not a problem.” Holmes counters by saying “rerunning the model and computing the Durbin-Watson statistic was unnecessary because serial correlation is never a problem in this type of time-series model.”

Briars and Holmes decide to ask their company’s statistician about the consequences of serial correlation. Based on what Briars and Holmes tell the statistician, the statistician informs them that serial correlation will only affect the standard errors and the coefficients are still unbiased. The statistician suggests that they employ the Hansen method, which corrects the standard errors for both serial correlation and heteroskedasticity.

Given the information from the statistician, Briars and Holmes decide to use the estimated coefficients to make some inferences. Holmes says the results do not look good for the future of tea imports because the coefficient on (Tea Import)t ? 1 is less than one. This means the process is mean reverting. Using the coefficients in the output, says Holmes, “we know that whenever tea imports are higher than 41.810, the next year they will tend to fall. Whenever the tea imports are less than 41.810, then they will tend to rise in the following year.” Briars agrees with the general assertion that the results suggest that imports will not grow in the long run and tend to revert to a long-run mean, but he says the actual long-run mean is 54.545. Briars then computes the forecast of imports three years into the future.

Part 6)

Given the output, the most obvious potential problem that Briars and Holmes need to investigate is:

A) a unit root.

B) conditional heteroskedasticity.

C) unconditional heteroskedasticity.

D) multicollinearity.

Clara Holmes, CFA, is attempting to model the importation of an herbal tea into the United States. She gathers 24 years of annual data, which is in millions of inflation-adjusted dollars. The real dollar value of the tea imports has grown steadily from $30 million in the first year of the sample to $54 million in the most recent year.

She computes the following equation:

(Tea Imports)t = 3.8836 + 0.9288 × (Tea Imports)t ? 1 + et

t-statistics (0.9328)(9.0025)

R2 = 0.7942

Adj. R2 = 0.7844

SE = 3.0892

N = 23

Holmes and her colleague, John Briars, CFA, discuss the implication of the model and how they might improve it. Holmes is fairly satisfied with the results because, as she says “the model explains 78.44 percent of the variation in the dependent variable.” Briars says the model actually explains more than that.

Briars asks about the Durbin-Watson statistic. Holmes said that she did not compute it, so Briars reruns the model and computes its value to be 2.1073. Briars says “now we know serial correlation is not a problem.” Holmes counters by saying “rerunning the model and computing the Durbin-Watson statistic was unnecessary because serial correlation is never a problem in this type of time-series model.”

Briars and Holmes decide to ask their company’s statistician about the consequences of serial correlation. Based on what Briars and Holmes tell the statistician, the statistician informs them that serial correlation will only affect the standard errors and the coefficients are still unbiased. The statistician suggests that they employ the Hansen method, which corrects the standard errors for both serial correlation and heteroskedasticity.

Given the information from the statistician, Briars and Holmes decide to use the estimated coefficients to make some inferences. Holmes says the results do not look good for the future of tea imports because the coefficient on (Tea Import)t ? 1 is less than one. This means the process is mean reverting. Using the coefficients in the output, says Holmes, “we know that whenever tea imports are higher than 41.810, the next year they will tend to fall. Whenever the tea imports are less than 41.810, then they will tend to rise in the following year.” Briars agrees with the general assertion that the results suggest that imports will not grow in the long run and tend to revert to a long-run mean, but he says the actual long-run mean is 54.545. Briars then computes the forecast of imports three years into the future.

Part 5)

With respect to the comments of Holmes and Briars concerning the mean reversion of the import data, the long-run mean value that:

A) Briars computes is correct, but the conclusion is probably not accurate.

B) Briars computes is not correct, but his conclusion is probably accurate.

C) Holmes computes is not correct, and her conclusion is probably not accurate.

D) Briars computes is correct, and his conclusion is probably accurate.

Clara Holmes, CFA, is attempting to model the importation of an herbal tea into the United States. She gathers 24 years of annual data, which is in millions of inflation-adjusted dollars. The real dollar value of the tea imports has grown steadily from $30 million in the first year of the sample to $54 million in the most recent year.

She computes the following equation:

(Tea Imports)t = 3.8836 + 0.9288 × (Tea Imports)t ? 1 + et

t-statistics (0.9328)(9.0025)

R2 = 0.7942

Adj. R2 = 0.7844

SE = 3.0892

N = 23

Holmes and her colleague, John Briars, CFA, discuss the implication of the model and how they might improve it. Holmes is fairly satisfied with the results because, as she says “the model explains 78.44 percent of the variation in the dependent variable.” Briars says the model actually explains more than that.

Briars asks about the Durbin-Watson statistic. Holmes said that she did not compute it, so Briars reruns the model and computes its value to be 2.1073. Briars says “now we know serial correlation is not a problem.” Holmes counters by saying “rerunning the model and computing the Durbin-Watson statistic was unnecessary because serial correlation is never a problem in this type of time-series model.”

Briars and Holmes decide to ask their company’s statistician about the consequences of serial correlation. Based on what Briars and Holmes tell the statistician, the statistician informs them that serial correlation will only affect the standard errors and the coefficients are still unbiased. The statistician suggests that they employ the Hansen method, which corrects the standard errors for both serial correlation and heteroskedasticity.

Given the information from the statistician, Briars and Holmes decide to use the estimated coefficients to make some inferences. Holmes says the results do not look good for the future of tea imports because the coefficient on (Tea Import)t ? 1 is less than one. This means the process is mean reverting. Using the coefficients in the output, says Holmes, “we know that whenever tea imports are higher than 41.810, the next year they will tend to fall. Whenever the tea imports are less than 41.810, then they will tend to rise in the following year.” Briars agrees with the general assertion that the results suggest that imports will not grow in the long run and tend to revert to a long-run mean, but he says the actual long-run mean is 54.545. Briars then computes the forecast of imports three years into the future.

Part 2)

With respect to the statement that the company’s statistician made concerning the consequences of serial correlation, assuming the company’s statistician is competent, we would most likely deduce that Holmes and Briars did not tell the statistician:

A) the sample size.

B) the value of the Durbin-Watson statistic.

C) that the intercept coefficient is not significant.

D) the model’s specification.

Clara Holmes, CFA, is attempting to model the importation of an herbal tea into the United States. She gathers 24 years of annual data, which is in millions of inflation-adjusted dollars. The real dollar value of the tea imports has grown steadily from $30 million in the first year of the sample to $54 million in the most recent year.

She computes the following equation:

(Tea Imports)t = 3.8836 + 0.9288 × (Tea Imports)t ? 1 + et

t-statistics (0.9328)(9.0025)

R2 = 0.7942

Adj. R2 = 0.7844

SE = 3.0892

N = 23

Holmes and her colleague, John Briars, CFA, discuss the implication of the model and how they might improve it. Holmes is fairly satisfied with the results because, as she says “the model explains 78.44 percent of the variation in the dependent variable.” Briars says the model actually explains more than that.

Briars asks about the Durbin-Watson statistic. Holmes said that she did not compute it, so Briars reruns the model and computes its value to be 2.1073. Briars says “now we know serial correlation is not a problem.” Holmes counters by saying “rerunning the model and computing the Durbin-Watson statistic was unnecessary because serial correlation is never a problem in this type of time-series model.”

Briars and Holmes decide to ask their company’s statistician about the consequences of serial correlation. Based on what Briars and Holmes tell the statistician, the statistician informs them that serial correlation will only affect the standard errors and the coefficients are still unbiased. The statistician suggests that they employ the Hansen method, which corrects the standard errors for both serial correlation and heteroskedasticity.

Given the information from the statistician, Briars and Holmes decide to use the estimated coefficients to make some inferences. Holmes says the results do not look good for the future of tea imports because the coefficient on (Tea Import)t ? 1 is less than one. This means the process is mean reverting. Using the coefficients in the output, says Holmes, “we know that whenever tea imports are higher than 41.810, the next year they will tend to fall. Whenever the tea imports are less than 41.810, then they will tend to rise in the following year.” Briars agrees with the general assertion that the results suggest that imports will not grow in the long run and tend to revert to a long-run mean, but he says the actual long-run mean is 54.545. Briars then computes the forecast of imports three years into the future.

Part 3)

The statistician’s statement concerning the benefits of the Hansen method is:

A) correct, because the Hansen method adjusts for problems associated with both serial correlation and heteroskedasticity.

B) not correct, because the Hansen method only adjusts for problems associated with serial correlation but not heteroskedasticity.

C) not correct, because the Hansen method only adjusts for problems associated with heteroskedasticity but not serial correlation.

D) not correct, because the Hansen method does not adjust for problems associated with either serial correlation or heteroskedasticity.

Clara Holmes, CFA, is attempting to model the importation of an herbal tea into the United States. She gathers 24 years of annual data, which is in millions of inflation-adjusted dollars. The real dollar value of the tea imports has grown steadily from $30 million in the first year of the sample to $54 million in the most recent year.

She computes the following equation:

(Tea Imports)t = 3.8836 + 0.9288 × (Tea Imports)t ? 1 + et

t-statistics (0.9328)(9.0025)

R2 = 0.7942

Adj. R2 = 0.7844

SE = 3.0892

N = 23

Holmes and her colleague, John Briars, CFA, discuss the implication of the model and how they might improve it. Holmes is fairly satisfied with the results because, as she says “the model explains 78.44 percent of the variation in the dependent variable.” Briars says the model actually explains more than that.

Briars asks about the Durbin-Watson statistic. Holmes said that she did not compute it, so Briars reruns the model and computes its value to be 2.1073. Briars says “now we know serial correlation is not a problem.” Holmes counters by saying “rerunning the model and computing the Durbin-Watson statistic was unnecessary because serial correlation is never a problem in this type of time-series model.”

Briars and Holmes decide to ask their company’s statistician about the consequences of serial correlation. Based on what Briars and Holmes tell the statistician, the statistician informs them that serial correlation will only affect the standard errors and the coefficients are still unbiased. The statistician suggests that they employ the Hansen method, which corrects the standard errors for both serial correlation and heteroskedasticity.

Given the information from the statistician, Briars and Holmes decide to use the estimated coefficients to make some inferences. Holmes says the results do not look good for the future of tea imports because the coefficient on (Tea Import)t ? 1 is less than one. This means the process is mean reverting. Using the coefficients in the output, says Holmes, “we know that whenever tea imports are higher than 41.810, the next year they will tend to fall. Whenever the tea imports are less than 41.810, then they will tend to rise in the following year.” Briars agrees with the general assertion that the results suggest that imports will not grow in the long run and tend to revert to a long-run mean, but he says the actual long-run mean is 54.545. Briars then computes the forecast of imports three years into the future.

Part 4)

Using the model’s results, Briar’s forecast for three years into the future is:

A) $54.108 million.

B) $47.151 million.

C) $51.450 million.

D) $54.543 million.

Question 4

Sally Fiedler, CFA, is a portfolio manager for Asipre Investments, Inc. In her spare time Fiedler intends to manage the small endowment fund for her children’s private day school. She believes it will only take a couple of hours each weekend and she will receive a discount on tuition for her two children. According to Standard IV(B) – Additional Compensation Arrangements, Fiedler:

A.must inform her employer of the arrangement but need not get permission as the time commitment is small.

B.must inform her employer and get permission, either verbal or written.

C.must inform her employer of all the details of this arrangement and receive written permission.

D.need not inform her employer because it will be on her own time and there is no actual compensation received.

Question 6

Kevin Blank, CFA, is a representative for Campbell Advisors. In a meeting with a prospective client, the client inquires about investing in bonds denominated in Mexican pesos. Blank assures the client that Campbell can help him with Mexican fixed income investing. In fact, Blank had heard that his colleague, Jon Woller, might have had experience in Mexican bonds. The following day Blank learns that Woller had, in fact, no such experience. Blank does not correct his earlier statement and the prospective client invests with Campbell. Blank has:

A.only violated the Code and Standards when he learned that his statement was incorrect and did not contact the prospect to explain his error.

B.violated the Code and Standards, both when he misrepresented the qualifications of his firm and later, when he learned the truth and failed to contact the prospective client and correct his earlier statement.

C.not violated the Code and Standards because Blank did not intentionally mislead the prospect.

D.not violated the Code and Standards because Blank’s statements were verbal and not in writing.

Yuan, CFA, is a portfolio manager, who has two clients. Both clients have the same amount of money in their accounts. Yuan thinks that a recession is coming, so he begins to increase the proportion of less risky assets in each account. In order to comply with Standard V (B), Communication with Clients and Prospective Clients, Yuan should:

A.ensure that he does the same investment strategy for both clients.

B.communicate with both clients about the change and inform them that the investment is based on his opinion.

C.do both of that.

Question 9

Abner Flome, CFA, is writing a research report on Paulsen Group, an investment advisory firm. Abner’s brother-in-law holds shares of Paulsen and Abner has recently interviewed for a position with Paulsen and expects a second interview. According to the Standards, Abner’s most appropriate action is to disclose:

A.his brother-in-law’s holding of Paulsen and the job offer to his supervisor and in the research report if he writes it.

B.his brother-in-law’s holding of Paulsen to his supervisor and in the research report if he writes it.

C.neither his brother-in-law’s holdings nor the job offer to his supervisor or in the report if he writes it.

D.the fact that he is being considered for a job at Paulsen to his supervisor and in the research report if he writes it.