Business Forecasting 8th Edition John Hanke, Dean Wichern - Solutions

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How did you decide on this model? (Note: Do not difference more than necessary. Too much differencing is indicated if low lag sample autocorrelations tend to increase in magnitude.)
How did you decide on this model? (Note: Do not difference more than necessary. Too much differencing is indicated if low lag sample autocorrelations tend to increase in magnitude.)
How did you decide on this model? (Note: Do not difference more thannecessary. Too much differencing is indicated if low lag sample autocorrelationstend to increase in magnitude.)
Should Julie combine forecasts?
In addition to choosing among the methods Julie tried, what other forecasting procedures would you suggest? Use Minitab or another forecasting software package to try other methods and compare them with your choice in Question 1.
Based on your choice in Question 1, write a detailed memo to Julie outlining your reasons for your choice, and indicate the extent to which you think this forecasting method would be effective,
Suppose you were recently hired by Alomega Food Stores and assigned to assist Julie in developing an effective forecasting method for monthly sales. After reviewing Julie's efforts to date, of the methods she tried, which would you recommend to her?
A colleague of Jill's suggested she try the Box- Jenkins ARIMA methodology. What advice do you have for Jill if she decides to try Box- Jenkins?
It's possible that the choice of forecasting method could shift to another technique as new quarterly data are added to the database. Should Jill rerun her entire analysis once in a while to check this? How often should this be done?
The optimum smoothing constants used by Holt's linear exponential smoothing were a = .722 and .722. As new data come in over the next few quarters, Jill should probably rerun her data to see if these values change. How often do you think she should do this?
Jill did not consider combining the forecasts generated by the three methods she analyzed. How would she go about doing so? What would be the advantages and disadvantages of such action?
Do you think Guy will accomplish his objectives with the Saturday meeting?
Is there some other approach that Guy might have tried, given his objectives?
What process do you think Guy should use after the hour's writing activities have been completed?
Can you think of any future business trends not discussed in this chapter that may influence the way forecasting is done in the future? Explain briefly.
Write a short response to the following assertions:a. Forecasts are always wrong, so why place any emphasis on demand planning?b. A good forecasting process is too expensive.c. All we need to do is hire a "quantitative type" to do our forecasting.
Why are neural networks viewed as a viable alternative to the other forecasting methods discussed in this text?
If you have access to a neural network program, try to find a network that produces better forecasts than the 2-4-1 network pre- sented here.
Find an article that describes an application of neural networks to time series forecasting. What method did the authors use, and how successful was it?
Should Julie use a simple average or weighted average approach to combining the forecasts?
Until Julie can experience each of these two methods in action. she is considering combining forecasts. She feels that this approach would counter office politics and still allow her to use a more scientific approach. Would this be a good idea?
How does the naive model compare to the mul- tiple regression model developed in Case 8-72
How accurate is Julie's naive forecasting model?
Are there any other methods they have over- looked while trying to research this matter?
What method would you suggest to the Goldens in utilizing the expertise of their friends to decide on the atmosphere and motif for their new restaurant?
Consider the actual sales shown in Table P-1, along with one-step-ahead forecasts produced by Winters' method and by a regression model.a. Construct the combined forecasts of sales produced by taking a simple average of the forecasts produced by Winters' method and the regression model.b. Construct
Identify two business situations where the Delphi method might be used to generate forecasts.
Write a report summarizing your findings. Include in your report a plot of the original series and the forecasts.
generate forecasts for the funding requirements for the next 12 months.
Using the model you have developed in part
Is the model suggested in part 1 adequate? Discuss with reference to residual plots, residual autocorrelations, and the Ljung-Box chi-square statistics. If the model is not adequate, modify and refit the initial model until you feel you have achieved a satisfactory model.
Using Minitab or equivalent software, fit an ARIMA(0, 1, 0)(0, 1, 1)12 model to the datal in Table 9-22. Do you think a constant term is required in the model? Explain.
Write a brief memo summarizing your findings.
Using your model. generate forecasts of revenue for the next 12 months. Append these forecasts to the end of the series and plot the results. Are you happy with the pattern of the forecasts?
Develop an ARIMA model for sales tax revenue using the Box-Jenkins methodology.
The Lydia Pinkham data are interesting due to the unique (unchanging) nature of the product and marketing for the 54-year period represented. What factors might affect annual sales data for automobiles and copper over this same period? Why?
There is some evidence that the Lydia Pinkham data may be nonstationary. For example. the sample autocorrelations tend to be large (persist) for several lags. Difference the data. Construct a time series plot of the differences. Using Minitab or a similar program, fit an ARIMA(1, 1.0) model to
After this analysis was completed. the figure for sales in 1961 became available: $1.426. What is the model's forecast for 1961? If this year were added to the testing data set, what would the revised MAPE and MAD be?
Using a program for ARIMA modeling, fit and check an ARIMA model for Mr. Tux sales. Generate forecasts for the next 12 months.
Given the autocorrelations in Figure 9-39 and the partial autocorrelations in Figure 9-40, what regular (nonseasonal) terms might John include in an ARIMA model for Mr. Tux sales? What seasonal terms might be included in the model?
Discuss the problems, if any, of explaining the Box-Jenkins method to John's banker and others on his management team.
Would you use the same Box-Jenkins model if the new data were combined with the old data?
How does your Box-Jenkins model compare to the regression models used in Chapter 8?
How do these forecasts compare with actual sales?
What are your forecasts for the first four weeks of January 1983?
What is the appropriate Box-Jenkins model to use on the original data?
Use the Box-Jenkins methodology to model and forecast the monthly gaso- line demand of Yukong Oil Company shown in Table P-17 in Problem 17 of Chapter 5.
(Hint: Consider a log transformation before modeling these data.)
Use the Box-Jenkins methodology to model and forecast the quarterly sales of Disney Company given in Table P-16 in Problem 16 of Chapter
(Hint: Consider a log transformation before modeling these data.)
Use the Box-Jenkins methodology to model and forecast the monthly sales of the Cavanaugh Company given in Table P-14 in Problem 14 of Chapter
Table P-15 gives the 120 monthly observations on the price in cents per bushel of corn in Omaha, Nebraska, Determine the best ARIMA model for these data. Generate forecasts of the price of corn for the next 12 months. Comment on the pattern of the forecasts.
The data in Table P-14 are the number of weekly automobile accidents for the years 1996 to 1997 in Havana County. Determine the appropriate ARIMA model and forecast accidents for the 91st week. Comment on the accuracy of your forecast.
Compare your forecasts with the actual prices using the MAPE. How accurate are your forecasts?
The data in Table P-13 are closing stock quotations for the DEF Corporation for 150 days. Determine the appropriate ARIMA model and forecast the stock price five days ahead from forecast origin =
The data in Table P-12 are weekly stock prices for IBM stock.a. Using a program for ARIMA modeling, obtain a plot of the data, the sample autocorrelations, and the sample partial autocorrelations. Use this information to tentatively identify an appropriate ARIMA model for this series.b. Is the IBM
Table P-11 contains a time series of 96 monthly observations. Using a com- puter program for ARIMA modeling, obtain a plot of the data, the sample?
Table P-10 contains a time series of 80 observations. Using a computer program for ARIMA modeling, obtain a plot of the data, the sample autocorrelations, and the sample partial autocorrelations. Develop an appropriate ARIMA model and generate forecasts for the next three time periods from forecast
Table P-9 contains a time series of 80 observations. Using a computer program for ARIMA modeling, obtain a plot of the data, the sample autocorrelations, and the sample partial autocorrelations. Develop an appropriate ARIMA model and generate forecasts for the next three time periods from forecast
Table P-8 contains a time series with 126 observations. Using a computer program for ARIMA modeling, obtain a plot of the data, the sample autocorrelations, and the sample partial autocorrelations. Develop an appropriate ARIMA model and generate forecasts for the next three time periods from
Also, construct 95% prediction intervals.
Chips Bakery has been having trouble forecasting the demand for its special high-fiber bread and would like your assistance. Data for the weekly demand, and the autocorrelations of the original data and various differences of the data, are shown in Tables P-7A-D.a. Inspect the autocorrelation plots
An ARIMA(1,1,0) model |AR(1) model for first differences] is fit to observations of a time series. The first 12 residual autocorrelations are shown in Figure P-6. The model was fit with a constant.a. Based on an inspection of the residual autocorrelations, does the model appear to be adequate? Why
Given the graphs in Figure P-5a-c of the sample autocorrelations and the sample partial autocorrelations, tentatively identify an ARIMA model from each pair of graphs.
Fill in the missing information in Table P-4. indicating whether the theoretical autocorrelations and partial autocorrelations die out or cut off for these models.
c. Suppose the estimate for the variance of the error term is 23.2. Compute a 95% prediction interval about the forecast for period 61.
Update the forecasts for periods 62 and
b. Suppose the observed value of Yet is
62, and 63 from origin
a. Determine forecasts for periods
A time series model has been fit and checked with historical data yielding Suppose at time = 60, the observation is You =
6. and 7 if period 4 is the forecast origin.
Suppose the following time series model has been fit to historical data and found to be an adequate model. Y = 35++.25,-1-.30-2 = The first four observations are Y = 32.5. Y = 36.6, Y = 33.3. and Y = 31.9. Assuming Y = 35 and to = 0, calculate forecasts for periods
a. For a sample of 100 observations of random data, calculate a 95% confidence interval for the autocorrelation coefficient at any lag.b. If all the autocorrelation coefficients are within their individual 95% confidence intervals and they show no particular pattern, what conclusion about the
What conditions might prompt Julie to reexam- ine her regression model or, perhaps, to look for another method of forecasting sales?
How might Julie's model be used to determine future amounts spent on newspaper and TV advertising?
Assuming there are no additional important predictor variables, are you satisfied with Julie's forecasting model? How would you "sell" the model to management (and Jackson Tilson)?
Julie has collected data on other variables that were not included in her multiple regression model. Should one or more of these other variables be included in her model? More generally, how can Julie be sure she has the "right" set of predictor variables?
Would another type of forecasting model be more effective for forecasting weekly sales?
Do you agree with Jim's conclusions?
Was it correct for Jim to use lagged sales as a predictor variable?
Was Jim's use of a dummy variable correct?
Is autocorrelation significant for models (3) and (4)? (Test at the .05 level.)
What conclusions can be drawn from a com- parison of the Spokane County business activity index and the GNP?
Is there any potential for the use of lagged data?
Should the regression done on the first differ- ences have been through the origin?
How does the small sample size affect the analysis?
Would it have been better to eliminate mul- ticollinearity first and then tackle autocorrela- tion?
Why did Young choose to solve the autocorre- lation problem first?
Circuit City Inc. is a retailer of video and audio equipment and other consumer electronics and office products. Recently, sales have been weak, declining by a total of 5% in December. Among the reasons were a fiercely competitive retailing environment among consumer-electronics sellers. price
A study is done in an attempt to relate personal savings to personal income (in billions of dollars) for the time period from 1935 to 1954. The data are given in Table P-18.a. Fit a simple linear regression model to the data in Table P-18 using per- sonal income to predict personal savings.
Refer to Example 8.5. Using the Sears data in Table 8-5. convert the sales and disposable income values to simple differences. That is, create the numbers YY-Y-1 and X, X, X. Fit a simple linear regression model to the differenced data. Compare your results to the results obtained by the method of
The data in Table P-16 show seasonally adjusted quarterly sales for Dickson Corporation and for the entire industry for 20 quarters.a. Fit a linear regression model, obtain the residuals, and plot the residuals against time. What do you find?
National Presto is a manufacturer of small electrical appliances, including pressure cookers, heaters, canners. fry pans, griddles, roaster ovens, deep fryers, corn poppers, can openers, coffee makers, slicers, hand mixers, and portable ranges. Its quarterly sales dollars in millions are shown in
Thomas Furniture Company concludes that production scheduling can be improved by developing an accurate method of predicting quarterly sales. The company ana- lyst, Mr. Estes, decides to investigate the relationship between housing construction permits and furniture sales in the Springfield area.
Thompson Airlines has determined that 5% of the total number of U.S. domestic airline passengers fly on Thompson planes. You are given the task of forecasting the number of passengers who will fly on Thompson Airlines in 2004. The data are presented in Table P-13.a. Develop a time series regression
Paul Raymond, president of Washington Water Power, is worried about the possi- bility of a takeover attempt and the fact that the number of common shareholders has been decreasing since 1983. He instructs you to study the number of common?
Jim Jackson, a rate analyst for the Washington Water Power Company, is preparing for a rate case and needs to forecast electric residential revenue for 1996. Jim decides to investigate three potential predictor variables: residential use per kilowatt-hour (kWh). residential charge per kWh
Decision Science Associates has been asked to do a feasibility study for a proposed destination resort to be located within half a mile of the Grand Coulee Dam. Mark Craze is not happy with the regression model that used the price of a regular gallon of gasoline to predict the number of visitors to
Tamson Russell, an economist working for the government, is trying to determine the demand function for passenger car motor fuel in the United States. Tamson developed a model that used the actual price of a gallon of regular gasoline to predict motor fuel consumed per year. She was able to explain

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Business Forecasting 8th Edition John Hanke, Dean Wichern - Solutions

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