Forecasting in Economics and Business (ECOM90024)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

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Semester 1
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This subject focuses on time series 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, pooled forecast procedures and forecast evaluation. The subject is applications-orientated.

Intended learning outcomes

On successful completion of this subject students should be able to:

  • Explain the main considerations involved in developing a successful forecasting model.
  • Implement and evaluate models for forecasting trends and seasonality.
  • Characterise, model and forecast cycles using moving average (MA), autoregressive (AR), and autoregressive moving average models (ARMA) for both stationary and non-stationary time series.
  • Implement and interpret point, interval, density and probability forecasts.
  • Apply common techniques to evaluate forecast performance and to combine forecasts from different models.
  • Demonstrate proficiency in time series data handling and modelling using statistical software.

Generic skills

  • 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: 27 February 2025