It must be submitted electronically via the blackboard no later than 5pm on Friday 7 September.
Late assignments will not be accepted and will attract a mark of 0. All submissions should go though the blackboard system.
The purpose of this assignment is to assess your knowledge of material in the first 5 weeks of ECMT3130. There are two questions for you to complete: question 1 will contribute 5% towards your final grade and question 2 will contribute 15% of your final grade; this gives a total of 20%.
Question 1 [12 points]
OmegaPlus Pty.Ltd. is a chain of Health Food stores operating in Australia: with 12 stores across Sydney, Melbourne and Brisbane. OmegaPlus has recently appointed a new CEO: Sandy Smith, who is open to the idea of quantitative forecasting methods, and is aware that various data have been collected for about 5 years now. However, quantitative forecasting has not been used at OmegaPlus before, mainly due to resistance by the Sales Manager: Dave Chen. Dave prefers talking to the shop owners and sales staff across the states to help assess trends and then forms ‘consensus’ forecasts from these discussions. The main quantity that requires forecasting at OmegaPlus is sales revenue.
- [6 points] List 3 important issues that must be discussed and decided upon BEFORE Sandy chooses between her favoured quantitative method and Dave Chen’s qualitative method for forecasting sales at OmegaPlus. Briefly explain why each issue is important to consider at this initial stage.
- [6 points] List 3 issues (AT MOST 1 can be the same as the 3 in part (i)) that would help OmegaPlus decide between quantitative and qualitative methods for forecasting sales. Explain why and how each issue will help to allow a decision between the two types of method in this case.
You may need to do a bit of extra reading to answer this question. There are plenty of references in the UoS outline to help you here.
Question 2 (adapted from Hanke and Wichern) [40 points] (Page limit for this part is 15 pages)
Australian Bureau of Statistics (ABS) provides retail data for different groups and different states as well as the aggregate numbers. The main webpage to access the retail statistics is
On the ‘Downloads’ tab you will find data for different categories. Table 11 “Retail Turnover, State by Industry Subgroup, Original” contains the data you can use in this part. You can select the state and the group you wish to analyse or you can use the aggregated data. The file contains monthly retail turnover data from April, 1982 to June, 2012. You are hired as a forecasting consultant to analyse the retail data as there is a concern that Australia retail sector is bad shape http://www.theaustralian.com.au/business/companies/retail-recovery-still-has-some-way-to-go/story-fn91v9q3-1226441773773
You, as an econometrics expert, need to analyse, generate and assess forecasts for monthly sales retail turnover for the rest of 2012.
After consulting with your peers, you decide to consider three models/methods for application to this dataset to generate forecasts.
C A model of your choice**.
- [4 points] Perform an exploratory analysis on the entire dataset and discuss the important aspects of the data. Show all RELEVANT output (e.g. graphs, tables).
- [2 points] Choose a suitable decomposition model for model B while clearly motivating your choice using your answer in part (i) and any other relevant information i.e. choose between a multiplicative OR an additive model.
- [1 point] Choose a suitable model C while again clearly motivating your choice*.
* Bonus marks are available for more complicated or formal models that are well chosen based on (i), if parts (iv)-(xiii) are done well, to acknowledge the extra work required for such models. However, simple models are still acceptable with full marks still possible for parts (iv)-(xiii) in that case.
You have to use at least 6 years as the in-sample period, and your hold-out sample for forecasting should have at least 12 points.
- [5 points] Clearly present and discuss the trend-cycle, seasonal and error components in the in-sample data, as assessed by model B. Contrast these components between the models B and C if appropriate.
- [3 points] Choose a model form for the trend component (one for Model B and also for model C, but only if appropriate) and clearly and properly justify your choice.