Please complete in Excel in forecasting…
Question 2: Suppose that you work for a U.S. senator who is contemplating writing bill that would put a national sales tax in place. Because the tax would be levied on the sales revenue of retail stores, the senator has asked you to prepare a forecast of retail store sales for year 8, based on data from year 1 through 7. The data are:
Year Retail Store Sales
1 $1,225
2 1,285
3 1,359
4 1,392
5 1,443
6 1,474
7 1,467
a.Use the first naive forecasting model presented in the chapter to prepare a forecast of retail store sales for each year from 2 through 8.
b.Prepare a time-series graph of the actual and forecast values of retail store sales for the entire period. (You will not have a forecast for year 1 or an actual value for year 8).
c.Calculate the root-mean-squared error for your forecast series using the values for year 2 through 7.
Question 3: Use the naive forecasting model presented in this chapter to answer parts (a)through (c)of Exercise 2. Use P= 0.2 in preparing the forecast. Which model do you think works the best? Explain why.
Question 5: Go to the library and look up annual data for population in the United States from 1981 through 2004. One good source for such data is the Economic Report of the President, published each year by the U.S. Government Printing Office. This series is also available at a number of Internet sites, includinghttp://www.economagic.com.
Plot the actual data along with the forecast you would get by using the first naive model discussed in this chapter. (Same method as used in steps (a) and (b) of Question 2).
Question 8: As the world economy becomes increasingly interdependent, various exchange rates between currencies have become important in making business decisions. For many U.S. businesses, the Japanese exchange rate (in yen per U.S. dollar) is an important decision variable. This exchange rate (EXRJ) is shown in the following table by month for a two-year period:
|
Period |
EXRJ |
|
Y1 M1 |
127.36 |
|
Y1 M2 |
127.74 |
|
Y1 M3 |
130.55 |
|
Y1 M4 |
132.04 |
|
Y1 M5 |
137.86 |
|
Y1 M6 |
143.98 |
|
Y1 M7 |
140.42 |
|
Y1 M8 |
141.49 |
|
Y1 M9 |
145.07 |
|
Y1 M10 |
142.21 |
|
Y1 M11 |
143.53 |
|
Y1 M12 |
143.69 |
|
Y2 M1 |
144.98 |
|
Y2 M2 |
145.69 |
|
Y2 M3 |
153.31 |
|
Y2 M4 |
158.46 |
|
Y2 M5 |
154.04 |
|
Y2 M6 |
153.7 |
|
Y2 M7 |
149.04 |
|
Y2 M8 |
147.46 |
|
Y2 M9 |
138.44 |
|
Y2 M10 |
129.59 |
|
Y2 M11 |
129.22 |
|
Y2 M12 |
133.89 |
Prepare a time-series plot of this series, and use the naive forecasting model to forecast EXRJ for each month from year 1 M2 (February) through year 3 M1 (January). Calculate the RMSE for the period from Y1 M2 through Y2 M12.
