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Homework answers / question archive / You have been employed as a consultant for a joint project by the Australian Food and Grocery Council and the Department of Agriculture, Water and the Environment

You have been employed as a consultant for a joint project by the Australian Food and Grocery Council and the Department of Agriculture, Water and the Environment. As part of your role in the Business Analytics and Data Analytics team, you have been asked to forecast Food Retailing as part of a wider report being commissioned by the above collaboration – on Australia’s food security. Questions ? Obtain the ABS statistics for Retail Trade - 8501.0 – available at: https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/8501.0June%202020?OpenDocument ? Download Table 1. ? For the purposes of this report you are to consider the Food Retailing data. There are three series in Table 1: Original, Seasonally-adjusted, and Trend. ? For the purposes of this report, only consider the data from January 2010 to December 2019 as the sample of data that is available to you – that is, ignore any recent observations. This means that the first actual observation in your Excel file is from January 2010 and your last actual observation in your Excel file is from December 2019. ? Use Excel and no other statistical software for the purposes of this report. This report will require two separate submissions. The numerical responses need to be submitted via a quiz tool in iLearn. The written responses need to be submitted via a PDF uploaded via Turn-It-In in iLearn. [Please turn over] 4 Numerical responses to be submitted via a quiz tool on iLearn: Exercise 1 (10 marks) For the Seasonally-adjusted data available in Table 1: Forecast food retailing for every month of 2020 using Holt’s Exponential Smoothing with the following parameters: alpha = 0.1 and beta = 0.1. For the seed of the level use the first actual observation. For the seed of the level and the trend – utilise the methods described in class. Once you perform Holt’s Exponential Smoothing with both alpha and beta equal to 0.1, what are the following numerical values: 1. The within-sample forecast for December 2019. 2. The out-of-sample forecast for January 2020. 3. The out-of-sample forecast for December 2020. 4. The MSE. 5. The MAE. Use the Solver tool in Excel to calculate the value of alpha and beta that minimises the MSE. After this optimisation, what are the following numerical values: 6. Alpha 7. Beta 8. The MSE 9. The out-of-sample forecast for January 2020. 10. The out-of-sample forecast for December 2020. [Please turn over] 5 Exercise 2 (10 marks) For the Original data available in Table 1: Forecast food retailing for every month of 2020 using Winter’s Exponential Smoothing (Multiplicative) with the following parameters: alpha = 0.1, beta = 0.1, and gamma = 0.1. For the seeds of the level, trend, and seasonal components – utilise the methods described in class. Once you perform Winter’s Exponential Smoothing with alpha, beta, and gamma all equal to 0.1, what are the following numerical values: 11. The seasonal component for December 2019. 12. The within-sample forecast for December 2019. 13. The out-of-sample forecast for December 2020. 14. The MSE. 15. The MAE. Use the Solver tool in Excel to calculate the value of alpha, beta, and gamma that minimises the MSE. After this optimisation, what are the following numerical values: 16. Alpha 17. Gamma 18. The MSE 19. The within-sample forecast for December 2019. 20. The out-of-sample forecast for December 2020. [Please turn over] 6 Written responses submitted via a PDF upload via Turn-It-In in iLearn: Exercise 3 (20 marks) 800 words (+/- 10%) not counting labels and numbers on graphs AND no more than three A4 sheets in portrait/vertical mode (use the template DOC file provided on iLearn): For Exercise 1 – Label this “Critical Analysis – Exercise 1” (5 marks) 21. Plot a chart that contains both the sample data (January 2010 – December 2019) and the forecasts (January 2020 – December 2020) – where there is a clear distinction between the sample data and the forecasts. 22. Critically analyse and comment on your smoothing. 23. Perform the appropriate check/s and test/s that help critically analyse whether your model has captured all the systematic components and/or whether the errors are random. Explain your answer. For Exercise 2 – Label this “Critical Analysis – Exercise 2” (5 marks) 24. Plot a chart that contains both the sample data (January 2010 – December 2019) and the forecasts (January 2020 – December 2020) – where there is a clear distinction between the sample data and the forecasts. 25. Critically analyse and comment on your smoothing. 26. Perform the appropriate check/s and test/s that help critically analyse whether your model has captured all the systematic components and/or whether the errors are random. Explain your answer. For both Exercise 1 and Exercise 2 – Label this “Critical Analysis, Evaluation, and Recommendation” (10 marks) 27. Compare and contrast the two models and critically analyse your results and put forward a recommendation for your choice of model. 28. Do any of your results (for any of the above questions) suggest any re-evaluation or modification of the forecasting method/s, if at all? Explain your answer. 29. In light of recent events, critically evaluate your choice of model, and critically evaluate the factors you would need to consider when forecasting in light of recent events. 7 30. Given that you have some of the actual data available for 2020, critically evaluate your forecasts and forecasting method. 31. In the context of business forecasting, critically think and discuss any other considerations that need to be taken into account for your forecasts / forecasting to be useful for business purposes.