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This subject is an introduction to single equation forecasting methods and their applications to business, finance, economics and marketing. Special emphasis will be given to core forecasting techniques with the widest applicability. Attention will be paid to modelling and forecasting trends and cycles. Topics may include forecasting regression models, leading indicators, exponential smoothing methods, ARIMA models, pooling forecast procedures and forecast evaluation. The subject is applications-orientated.
Students are required to attend a minimum of 80% of classes and tutorials in order to pass this subject and regular class participation is expected.
Intended learning outcomes
On successful completion of this subject students should be able to:
- Explain the main considerations of a successful forecasting model
- Model and forecast trends
- Model and forecast seasonality
- Characterise, model and forecast cycles using moving average (MA), autoregressive (AR), and autoregressive moving average models (ARMA)
- Define unit root processes, stochastic trends and model using autoregressive, integrated, moving average processes (ARIMA)
- Model empirical processes using software
High level of development: problem solving; statistical reasoning; interpretation and analysis; critical thinking; synthesis of data and other information; evaluation of data and other information; use of computer software; accessing data and other information from a range of sources.
Moderate level of development: oral communication; team work; application of theory to practice; receptiveness to alternative ideas.
Some level of development: written communication; collaborative learning.
Last updated: 4 August 2022