Ethical And Professional Standards Quantitative Methods And Economics Level II Volume 1 CFA Institute - Solutions

describe overfitting and identify methods of addressing it;
distinguish between supervised machine learning, unsupervised machine learning, and deep learning;
Based on Exhibit 5, which single time-series model would most likely be appropriate for Busse to use in predicting the future stock price of Company #3?A. Log-linear trend model B. First-differenced AR(2) model C. First-differenced log AR(1) model
Based on Exhibit 5, for which company would the regression of stock prices on oil prices be expected to yield valid coefficients that could be used to estimate the long-term relationship between stock price and oil price?A. Company #1 B. Company #2 C. Company #3
Based on Exhibit 5, Busse should conclude that the variance of the error terms for Company#1:A. is constant.B. can be predicted.C. is homoskedastic.
Based on the regression output in Exhibit 3 and sales data in Exhibit 4, the forecasted value of quarterly sales for March 2016 for PoweredUP is closest to:A. $4.193 billion.B. $4.205 billion.C. $4.231 billion.
Based on the regression output in Exhibit 2, what should lead Busse to conclude that the Regression 3 equation is not correctly specified?A. The Durbin–Watson statistic B. The t-statistic for the slope coefficient C. The t-statistics for the autocorrelations of the residual
In order to perform the nonstationarity test, Busse should transform the Regression 1 equation by:A. adding the second lag to the equation.B. changing the regression’s independent variable.C. subtracting the independent variable from both sides of the equation.
Based on the regression results in Exhibit 1, the original time series of exchange rates:A. has a unit root.B. exhibits stationarity.C. can be modeled using linear regression.
Based on the regression output in Exhibit 1, the first-differenced series used to run Regression 2 is consistent with:A. a random walk.B. covariance stationarity.C. a random walk with drift.
Which of Busse’s conclusions regarding the exchange rate time series is consistent with both the properties of a covariance-stationary time series and the properties of a random walk?A. Conclusion 1 B. Conclusion 2 C. Conclusion 3
Based on the mean-reverting level implied by the AR(1) model regression output in Exhibit 1, the forecasted oil price for September 2015 is most likely to be:A. less than $42.86.B. equal to $42.86.C. greater than $42.86.
Based on the data for the AR(1) model in Exhibits 1 and 2, Martinez can conclude that the:A. residuals are not serially correlated.B. autocorrelations do not differ significantly from zero.C. standard error for each of the autocorrelations is 0.0745.
Based on Exhibit 1, the forecasted oil price in September 2015 based on the AR(2) model is closest to:A. $38.03.B. $40.04.C. $61.77.
Martinez’s Conclusion 1 is:A. correct.B. incorrect because the mean and variance of WTI oil prices are not constant over time.C. incorrect because the Durbin–Watson statistic of the AR(2) model is greater than 1.75.
Based on the regression output in Exhibit 1, there is evidence of positive serial correlation in the errors in:A. the linear trend model but not the log-linear trend model.B. both the linear trend model and the log-linear trend model.C. neither the linear trend model nor the log-linear trend model.
Based on Exhibit 1, the predicted WTI oil price for September 2015 using the log-linear trend model is closest to:A. $29.75.B. $29.98.C. $116.50.
Based on Exhibit 1, the predicted WTI oil price for October 2015 using the linear trend model is closest to:A. $29.15.B. $74.77.C. $103.10.
Suppose we want to predict the annualized return of the five-year T-bill using the annualized return of the three-month T-bill with monthly observations from January 1993 to December 2002. Our analysis produces the data shown in Table 10.Table 10. Regression with 3-Month T-Bill as the Independent
Describe how to test for autoregressive conditional heteroskedasticity (ARCH) in the residuals from the AR(1) regression on first differences in the civilian unemployment rate,ΔUERt = b0 + b1ΔUERt–1 + εt.
Suppose we decide to use an autoregressive model with a seasonal lag because of the seasonal autocorrelation in the previous problem. We are modeling quarterly data, so we estimate Equation 15: (ln Salest – ln Salest–1) = b0 + b1(ln Salest–1 – ln Salest–2) + b2(ln Salest–4 – ln
Table 7 below shows the autocorrelations of the residuals from an AR(1) model fit to the changes in the gross profit margin (GPM) of The Home Depot, Inc.Table 7. Autocorrelations of the Residuals from Estimating the RegressionΔGPMt = 0.0006 – 0.33301ΔGPMt–1 + εt 1Q:1992–4Q:2001 (40
Figure 7 shows the quarterly sales of Avon Products from 1Q:1992 to 2Q:2002. Describe the salient features of the data shown.Figure 7. Quarterly Sales at Avon
Figure 6 shows the quarterly sales of Cisco Systems from 1Q:1991 to 4Q:2000.Figure 6. Quarterly Sales at Cisco Table 6 gives the regression statistics from estimating the model Δln (Salest) = b0 + b1Δln(Salest–1) + εt.Table 6. Change in the Natural Log of Sales for Cisco Systems Quarterly
Using monthly data from January 1992 to December 2000, we estimate the following equation for lightweight vehicle sales: Δln (Salest) = 2.7108 + 0.3987Δln (Salest–1) + εt.Table 5 gives sample autocorrelations of the errors from this model.Table 5. Different Order Autocorrelations of
Figure 5 shows a plot of first differences in the log of monthly lightweight vehicle sales over the same period as in Problem 11. Has differencing the data made the resulting series, Δln(Salest) = ln (Salest) – ln (Salest–1), covariance stationary?Figure 5. Change in Natural Log of Lightweight
Figure 4 shows monthly observations on the natural log of lightweight vehicle sales, ln(Salest), for the period January 1992 to December 2000.Figure 4. Lightweight Vehicle Sales A. Using the figure, comment on whether the specification ln (Salest) = b0 + b1[ln (Salest–1)] + εt is appropriate.B.
A. The AR(1) model for the civilian unemployment rate, ΔUERt = –0.0405 –0.4674ΔUERt–1, was developed with five years of data. What would be the drawback to using the AR(1) model to predict changes in the civilian unemployment rate 12 months or more ahead, as compared with one month ahead?B.
Table 4 gives the actual change in the log of sales of Cisco Systems from 1Q:2001 to 4Q:2001, along with the forecasts from the regression model Δln (Salest) = 0.0661 +0.4698Δln (Salest–1) estimated using data from 3Q:1991 to 4Q:2000. (Note that the observations after the fourth quarter of 2000
Table 3 gives the actual sales, log of sales, and changes in the log of sales of Cisco Systems for the period 1Q:2001 to 4Q:2001.Table 3 Date Quarter: Year Actual Sales($ Millions) Log of Sales Changes in Log of SalesΔln (Salest)1Q:2001 6,519 8.7825 0.1308 2Q:2001 6,748 8.8170 0.0345 3Q:2001 4,728
Suppose the following model describes changes in the civilian unemployment rate: ΔUERt =–0.0405 – 0.4674ΔUERt–1. The current change (first difference) in the unemployment rate is 0.0300. Assume that the mean-reverting level for changes in the unemployment rate is –0.0276.A. What is the
Assume that changes in the civilian unemployment rate are covariance stationary and that an AR(1) model is a good description for the time series of changes in the unemployment rate.Specifically, we have ΔUERt = –0.0405 – 0.4674ΔUERt–1 (using the coefficient estimates given in the previous
Table 2 gives the regression output of an AR(1) model on first differences in the unemployment rate. Describe how to interpret the DW statistic for this regression.Table 2. Estimating an AR(1) Model of Changes in the Civilian Unemployment Rate Monthly Observations, March 1996–December 2000
Figure 3 shows a plot of the first differences in the civilian unemployment rate (UER)between January 1996 and December 2000, ΔUERt = UERt – UERt–1.Figure 3. Change in Civilian Unemployment Rate A. Has differencing the data made the new series, ΔUERt, covariance stationary? Explain your
You have been assigned to analyze automobile manufacturers and as a first step in your analysis, you decide to model monthly sales of lightweight vehicles to determine sales growth in that part of the industry. Figure 2 gives lightweight vehicle monthly sales(annualized) from January 1992 to
Figure 1 compares the predicted civilian unemployment rate (PRED) with the actual civilian unemployment rate (UER) from January 1996 to December 2000. The predicted results come from estimating the linear time trend model UERt = b0 + b1t + εt.What can we conclude about the appropriateness of this
The civilian unemployment rate (UER) is an important component of many economic models. Table 1 gives regression statistics from estimating a linear trend model of the unemployment rate: UERt = b0 + b1t + εt.Table 1. Estimating a Linear Trend in the Civilian Unemployment Rate Monthly Observations,
AstraZenca’s sales in the third and fourth quarters of 2011 were $8,405 million and$8,872 million, respectively. If we use the above model soon after the end of the fourth quarter of 2011, what will be the predicted value of AstraZenca’s sales for the first quarter of 2012?
Explain how to interpret the estimated coefficients in the model.
The analyst selected an AR(2) model because the residuals from the AR(1) model were serially correlated. Suppose that in a given month, inflation had been 4 percent at an annual rate in the previous month and 3 percent in the month before that. What would be the difference in the analyst forecast
How much different is the above forecast from the prediction of the linear trend model?
Benedict wants to use the results of estimating Equation 3 to predict Starbucks’ sales in the future. What is the predicted value of Starbucks’ sales for the first quarter of 2014?
determine an appropriate time-series model to analyze a given investment problem and justify that choice.
explain how time-series variables should be analyzed for nonstationarity and/or cointegration before use in a linear regression;
explain autoregressive conditional heteroskedasticity (ARCH) and describe how ARCH models can be applied to predict the variance of a time series;
explain how to test and correct for seasonality in a time-series model and calculate and interpret a forecasted value using an AR model with a seasonal lag;
describe the steps of the unit root test for nonstationarity and explain the relation of the test to autoregressive time-series models;
describe implications of unit roots for time-series analysis, explain when unit roots are likely to occur and how to test for them, and demonstrate how a time series with a unit root can be transformed so it can be analyzed with an AR model;
describe characteristics of random walk processes and contrast them to covariance stationary processes;
explain the instability of coefficients of time-series models;
contrast in-sample and out-of-sample forecasts and compare the forecasting accuracy of different time-series models based on the root mean squared error criterion;
explain mean reversion and calculate a mean-reverting level;
explain how autocorrelations of the residuals can be used to test whether the autoregressive model fits the time series;
describe the structure of an autoregressive (AR) model of order p and calculate one- and two-period-ahead forecasts given the estimated coefficients;
explain the requirement for a time series to be covariance stationary and describe the significance of a series that is not stationary;
describe factors that determine whether a linear or a log-linear trend should be used with a particular time series and evaluate limitations of trend models;
calculate and evaluate the predicted trend value for a time series, modeled as either a linear trend or a log-linear trend, given the estimated trend coefficients;
The best rationale for Quinni’s caution about the three-variable model is that the:A. dependent variable is defined differently.B. sample sizes are different in the two models.C. dividend growth rate is positively correlated with the other independent variables.
If Varden’s beliefs about ROE and CEO tenure are true, which of the following would violate the assumptions of multiple regression analysis?A. The assumption about CEO tenure distribution only B. The assumption about the ROE/dividend growth correlation only C. The assumptions about both the
Varden’s best answer to Quinni’s question about overall significance is:A. R2.B. adjusted R2.C. the F-statistic.
Based on Exhibit 1, Varden’s best answer to Quinni’s question about the Fstatistic is:A. both independent variables are significant at the 0.05 level.B. neither independent variable is significant at the 0.05 level.C. at least one independent variable is significant at the 0.05 level.
Based on Exhibit 2, Quinni’s best answer to Varden’s question about the effect of adding a third independent variable is:A. no for R2 and no for adjusted R2.B. yes for R2 and no for adjusted R2.C. yes for R2 and yes for adjusted R2.
Based on Exhibit 1, the predicted ROE for DF Associates is closest to:A. 10.957%.B. 16.593%.C. 20.388%.
Based on Exhibit 1, which independent variables in Varden’s model are significant at the 0.05 level?A. ESG only B. Tenure only C. Neither ESG nor tenure
At a significance level of 1%, which of the following is the best interpretation of the regression coefficients with regard to explaining ROE?A. ESG is significant, but tenure is not.B. Tenure is significant, but ESG is not.C. Neither ESG nor tenure is significant.
Based on Exhibit 1 and given Varden’s expectations, which is the best null hypothesis and conclusion regarding CEO tenure?A. b2 ≤ 0; reject the null hypothesis B. b2 = 0; cannot reject the null hypothesis C. b2 ≥ 0; reject the null hypothesis
In interpreting the overall significance of your regression model, which statistic do you believe is most relevant: R2, adjusted R2, or the F-statistic?
What does your F-statistic of 4.161 tell you about the regression?
Should Honoré have estimated the models in Exhibit 1 and Exhibit 2 using probit or logit models instead of traditional regression analysis?A. Both should be estimated with probit or logit models.B. Neither should be estimated with probit or logit models.C. Only the analysis in Exhibit 1 should be
Based on her estimated Durbin–Watson statistic, Honoré should:A. fail to reject the null hypothesis.B. reject the null hypothesis because there is significant positive serial correlation.C. reject the null hypothesis because there is significant negative serial correlation.
Is Honoré’s description of the effects of positive serial correlation (in Exhibit 2)correct regarding the estimated coefficients and the standard errors?A. Yes B. No, she is incorrect about only the estimated coefficients C. No, she is incorrect about only the standard errors of the regression
Honoré is concerned about the consequences of heteroskedasticity. Is she correct regarding the effect of heteroskedasticity on the reliability of the F-test and ttests?A. Yes B. No, she is incorrect with regard to the F-test C. No, she is incorrect with regard to the t-tests
Which of the three methods suggested by Smith would best capture the ability of the Morningstar rating system to predict mutual fund performance?A. Method 1 B. Method 2 C. Method 3
Honoré describes three potential consequences of multicollinearity. Are all three consequences correct?A. Yes B. No, 1 is incorrect C. No, 2 is incorrect
Based on Exhibit 1, the difference between the predicted annualized returns of a growth fund and an otherwise similar value fund is closest to:A. 1.86%.B. 2.44%.C. 3.01%.
Considering Exhibit 1, the F-statistic is closest to:A. 3.22.B. 8.06.C. 30.79.
Is Chiesa’s concluding statement correct regarding parameter estimate uncertainty and regression model uncertainty?A. Yes.B. No, predictions are not subject to parameter estimate uncertainty.C. No, predictions are subject to regression model uncertainty and parameter estimate uncertainty.
With respect to the default spread, the estimated model indicates that when business conditions are:A. strong, expected excess returns will be higher.B. weak, expected excess returns will be lower.C. weak, expected excess returns will be higher.
In response to Question 4, the 95 percent confidence interval for the regression coefficient for the default spread is closest to:A. 0.13 to 5.95.B. 1.72 to 4.36.C. 1.93 to 4.15.
Regarding Question 3, the Pres party dummy variable in the model indicates that the mean monthly value for the excess stock market return is:A. 1.43 percent larger during Democratic presidencies than Republican presidencies.B. 3.17 percent larger during Democratic presidencies than Republican
Which of the following is Chiesa’s best response to Question 2 regarding serial correlation in the error term? At a 0.05 level of significance, the test for serial correlation indicates that there is:A. no serial correlation in the error term.B. positive serial correlation in the error term.C.
Regarding the intern’s Question 1, is the regression model as a whole significant at the 0.05 level?A. No, because the calculated F-statistic is less than the critical value for F.B. Yes, because the calculated F-statistic is greater than the critical value for F.C. Yes, because the calculated
Default spread appears to be quite important. Is there some way to assess the precision of its estimated coefficient? What is the economic interpretation of this variable?
How do you interpret the coefficient for the Pres party dummy variable?
Does the estimated model conform to standard regression assumptions? For instance, is the error term serially correlated, or is there conditional heteroskedasticity?
How do you test to determine whether the overall regression model is significant?
Is Chang’s Statement 2 correct?A. Yes.B. No, because the model’s coefficient estimates will be unbiased.C. No, because the model’s coefficient estimates will be consistent
Is Chang’s Statement 1 correct?A. Yes.B. No, because the model’s F-statistic will not be biased.C. No, because the model’s t-statistics will not be biased.
The most appropriate interpretation of the multiple R-squared for Hansen’s model is that:A. unexplained variation in the dependent variable is 36 percent of total variation.B. correlation between predicted and actual values of the dependent variable is 0.36.C. correlation between predicted and
The most appropriate null hypothesis and the most appropriate conclusion regarding Hansen’s belief about the magnitude of the initial return relative to that of the pre-offer price adjustment (reflected by the coefficient bj) are:Null Hypothesis Conclusion about bj(0.05 Level of Significance)A
The 95 percent confidence interval for the regression coefficient for the pre-offer price adjustment is closest to:A. 0.156 to 0.714.B. 0.395 to 0.475.C. 0.402 to 0.468.
Based on Hansen’s regression, the predicted initial return for the upcoming IPO is closest to:A. 0.0943.B. 0.1064.C. 0.1541.
You have noticed that hundreds of non-US companies are listed not only on a stock exchange in their home market but also on one of the exchanges in the United States. You have also noticed that hundreds of non-US companies are listed only in their home market and not in the United States. You are
You are analyzing the cross-sectional variation in the number of financial analysts that follow a company (also the subject of Problems 3 and 8). You believe that there is less analyst following for companies with a greater debt-to-equity ratio and greater analyst following for companies included
You are analyzing the variables that explain the returns on the stock of the Boeing Company. Because overall market returns are likely to explain a part of the returns on Boeing, you decide to include the returns on a value-weighted index of all the companies listed on the NYSE, AMEX, and NASDAQ as
The book-to-market ratio and the size of a company’s equity are two factors that have been asserted to be useful in explaining the cross-sectional variation in subsequent returns. Based on this assertion, you want to estimate the following regression model:where Ri = Return of company i’s
In estimating a regression based on monthly observations from January 1987 to December 2002 inclusive, you find that the coefficient on the independent variable is positive and significant at the 0.05 level. You are concerned, however, that the t-statistic on the independent variable may be
You are analyzing if institutional investors such as mutual funds and pension funds prefer to hold shares of companies with less volatile returns. You have the percentage of shares held by institutional investors at the end of 1998 for a random sample of 750 companies. For these companies, you
You are examining the effects of the January 2001 NYSE implementation of the trading of shares in minimal increments (ticks) of $0.01 (decimalization). In particular, you are analyzing a sample of 52 Canadian companies cross-listed on both the NYSE and the Toronto Stock Exchange (TSE). You find
You believe there is a relationship between book-to-market ratios and subsequent returns. The output from a cross-sectional regression and a graph of the actual and predicted relationship between the book-to-market ratio and return are shown below.Results from Regressing Returns on the

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Ethical And Professional Standards Quantitative Methods And Economics Level II Volume 1 CFA Institute - Solutions

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