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  3. Time Series Analysis and Forecasting

Time Series Analysis and Forecasting (ECOM90004)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 2
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Normally topics will include current techniques used in forecasting in finance, accounting and economics such as regression models, Box-Jenkins, ARIMA models, vector autoregression, causality analysis, cointegration and forecast evaluation, ARCH models. 

Intended learning outcomes

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

  • Apply the Box-Jenkins methodology for identifying stationary and non-stationary univariate forecasting models,
  • Apply VAR/VECM models to analyse relationships between economic and financial time series,
  • Apply ARCH models to analyse and forecast the volatility of financial time series.

Generic skills

On successful completion of this subject, students should have improved the following generic skills:

  • Evaluation of ideas, views and evidence
  • Synthesis of ideas, views and evidence
  • Strategic thinking
  • Critical thinking
  • Accessing economic and other information
  • Summary and interpretation of information
  • Application of Windows software
  • Statistical reasoning
  • Problem solving skills
  • Written communication

Last updated: 8 November 2024