This unit entry is for students who completed this unit in 2016 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

ETF5231 - Business forecasting

6 points, SCA Band 2, 0.125 EFTSL

Postgraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

Faculty

Business and Economics

Organisational Unit

Department of Econometrics and Business Statistics

Coordinator(s)

Associate Professor George Athanasopoulos

Offered

Caulfield

First semester 2016 (Day)

Synopsis

Forecasts are crucial for guiding the planning and decision making process in business. In this unit you will learn to apply reliable methods for generating accurate forecasts in a rapidly changing business environment. These include: an introduction to regression modelling from a forecasting perspective, classical decomposition, exponential smoothing, Box-Jenkins ARIMA modelling and judgemental forecasting. You will build your programming skills by learning to program in R, a free programming language for statistical computing.

Outcomes

The learning goals associated with this unit are to:

develop statistical skills for analysing data in a business environment
learn how to build accurate and robust models for forecasting
acquire computer skills vital for forecasting business and economic data
gain knowledge of the theoretical aspects of the models used.

Assessment

Within semester assessment: 40%
Examination: 60%

Workload requirements

Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning, peer directed learning and online engagement.

Chief examiner(s)

Associate Professor George Athanasopoulos

Prerequisites

Students must have passed ETX2111, ETF2100, ETX2121, ETF2121, ETF9100, ETF5910, ETF9121, ETF5912, ETF9300, ETF5930, MBA9007, MBA5907 or equivalent.

Prohibitions

ETX3231, ETC2450, ETF3231