BUSINESS FINANCE FORECASTING WITH EXCEL
BUSINESS FINANCE FORECASTING WITH EXCEL.
6. The following inventory pattern has been observed in the Zahm Corporation over 12 months:
Use both three-month and five-month moving-average models to forecast the inventory for the next January. Use root-mean-squared error (RMSE) to evaluate these two forecasts.
11. a. Plot the data presented in Exercise 7 to examine the possible existence of trend and seasonality in the data.
b. Prepare four separate smoothing models to examine the full-service restaurant sales data using the monthly data.
1. A simple smoothing model
2. Holt’s model
3. Winters’ model
c. Examine the accuracy of each model by calculating the root-mean-squared error for each during the historical period. Explain carefully what characteristics of the original data led one of these models to minimize the root-mean-squared error.
13. The data in the table below are for retail sales in book stores by quarter.
a. Plot these data and examine the plot. Does this view of the data suggest a particular smoothing model? Do the data appear to be seasonal? Explain.
b. Use a smoothing method to forecast the next four quarters. Plot the actual and forecast values.
**duplicate Figure 3.13
4. The following regression results relate to a study of the salaries of public school teachers in a midwestern city:
a. What is the t-ratio for EXP? Does it indicate that experience is a statistically significant determinant of salary if a 95 percent confidence level is desired?
b. What percentage of the variation in salary is explained by this model?
c. Determine the point estimate of salary for a teacher with 20 years of experience.
d. What is the approximate 95 percent confidence interval for your point estimate from part (c)?
6. Mid-Valley Travel Agency (MVTA) has offices in 12 cities. The company believes that its monthly airline bookings are related to the mean income in those cities and has collected the following data:
a. Develop a linear regression model of monthly airline bookings as a function of income.
b. Use the process described in the chapter to evaluate your results.
c. Make the point and approximate 95 percent confidence interval estimates of monthly airline bookings for another city in which MVTA is considering opening a branch, given that income in that city is $39,020.
9. Carolina Wood Products, Inc., a major manufacturer of household furniture, is interested in predicting expenditures on furniture (FURN) for the entire United States. It has the following data by quarter for 1998 through 2007:
a. Prepare a naive forecast for 2008Q1 based on the following model (see Chapter 1):
b. Estimate the bivariate linear trend model for the data where TIME 1 for 1998Q1 through TIME 40 for 2007Q4.
c. Write a paragraph in which you evaluate this model, with particular emphasis on its usefulness in forecasting.
d. Prepare a time-trend forecast of furniture and household equipment expenditures for 2008 based on the model in part (b).
e. Suppose that the actual values of FURN for 2008 were as shown in the following table. Calculate the RMSE for both of your forecasts and interpret the results. (For the naive forecast, there will be only one observation, for 2008Q1.)
10. Fifteen midwestern and mountain states have united in an effort to promote and forecast tourism. One aspect of their work has been related to the dollar amount spent per year on domestic travel (DTE) in each state. They have the following estimates for disposable personal income per capita (DPI) and DTE:
a. From these data estimate a bivariate linear regression equation for domestic travel expenditures (DTE) as a function of disposable income per capita (DPI):
Evaluate the statistical significance of this model.
b. Illinois, a bordering state, has asked that this model be used to forecast DTE for Illinois under the assumption that DPI will be $19,648. Make the appropriate point and approximate 95 percent interval estimates.
c. Given that actual DTE turned out to be $7,754 (million), calculate the percentage error in your forecast.
11. Collect data on population for your state (http://www.economagic.com may be a good source for these data) over the past 20 years and use a bivariate regression trend line to forecast population for the next five years. Prepare a time-series plot that shows both actual and forecast values. Do you think the model looks as though it will provide reasonably accurate forecasts for the five-year horizon? (c4p11)
14. The following data are for shoe store sales in the United States in millions of dollars after being seasonally adjusted (SASSS).
a. Make a linear trend forecast for SASSS though the first seven months of 2007. Given that the actual seasonally adjusted values for 2007 were the following, calculate the RMSE for 2007.
b. Reseasonalize the 2007 forecast and the 2007 actual sales using the following seasonal indices:
c. Plot the final forecast along with the actual sales data. Does the forecast appear reasonable? Explain.
d. Why do you think the April, May, August, and December seasonal indices are greater than 1?
Use both three-month and five-month moving-average models to forecast the inventory for the next January. Use root-mean-squared error (RMSE) to evaluate these two forecasts.
11. a. Plot the data presented in Exercise 7 to examine the possible existence of trend and seasonality in the data.
b. Prepare four separate smoothing models to examine the full-service restaurant sales data using the monthly data.
1. A simple smoothing model
2. Holt’s model
3. Winters’ model
c. Examine the accuracy of each model by calculating the root-mean-squared error for each during the historical period. Explain carefully what characteristics of the original data led one of these models to minimize the root-mean-squared error.
13. The data in the table below are for retail sales in book stores by quarter.
a. Plot these data and examine the plot. Does this view of the data suggest a particular smoothing model? Do the data appear to be seasonal? Explain.
b. Use a smoothing method to forecast the next four quarters. Plot the actual and forecast values.
**duplicate Figure 3.13
4. The following regression results relate to a study of the salaries of public school teachers in a midwestern city:
a. What is the t-ratio for EXP? Does it indicate that experience is a statistically significant determinant of salary if a 95 percent confidence level is desired?
b. What percentage of the variation in salary is explained by this model?
c. Determine the point estimate of salary for a teacher with 20 years of experience.
d. What is the approximate 95 percent confidence interval for your point estimate from part (c)?
6. Mid-Valley Travel Agency (MVTA) has offices in 12 cities. The company believes that its monthly airline bookings are related to the mean income in those cities and has collected the following data:
a. Develop a linear regression model of monthly airline bookings as a function of income.
b. Use the process described in the chapter to evaluate your results.
c. Make the point and approximate 95 percent confidence interval estimates of monthly airline bookings for another city in which MVTA is considering opening a branch, given that income in that city is $39,020.
9. Carolina Wood Products, Inc., a major manufacturer of household furniture, is interested in predicting expenditures on furniture (FURN) for the entire United States. It has the following data by quarter for 1998 through 2007:
a. Prepare a naive forecast for 2008Q1 based on the following model (see Chapter 1):
b. Estimate the bivariate linear trend model for the data where TIME 1 for 1998Q1 through TIME 40 for 2007Q4.
c. Write a paragraph in which you evaluate this model, with particular emphasis on its usefulness in forecasting.
d. Prepare a time-trend forecast of furniture and household equipment expenditures for 2008 based on the model in part (b).
e. Suppose that the actual values of FURN for 2008 were as shown in the following table. Calculate the RMSE for both of your forecasts and interpret the results. (For the naive forecast, there will be only one observation, for 2008Q1.)
10. Fifteen midwestern and mountain states have united in an effort to promote and forecast tourism. One aspect of their work has been related to the dollar amount spent per year on domestic travel (DTE) in each state. They have the following estimates for disposable personal income per capita (DPI) and DTE:
a. From these data estimate a bivariate linear regression equation for domestic travel expenditures (DTE) as a function of disposable income per capita (DPI):
Evaluate the statistical significance of this model.
b. Illinois, a bordering state, has asked that this model be used to forecast DTE for Illinois under the assumption that DPI will be $19,648. Make the appropriate point and approximate 95 percent interval estimates.
c. Given that actual DTE turned out to be $7,754 (million), calculate the percentage error in your forecast.
11. Collect data on population for your state (http://www.economagic.com may be a good source for these data) over the past 20 years and use a bivariate regression trend line to forecast population for the next five years. Prepare a time-series plot that shows both actual and forecast values. Do you think the model looks as though it will provide reasonably accurate forecasts for the five-year horizon? (c4p11)
14. The following data are for shoe store sales in the United States in millions of dollars after being seasonally adjusted (SASSS).
a. Make a linear trend forecast for SASSS though the first seven months of 2007. Given that the actual seasonally adjusted values for 2007 were the following, calculate the RMSE for 2007.
b. Reseasonalize the 2007 forecast and the 2007 actual sales using the following seasonal indices:
c. Plot the final forecast along with the actual sales data. Does the forecast appear reasonable? Explain.
d. Why do you think the April, May, August, and December seasonal indices are greater than 1?
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