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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. The computer software used is EVIEWS.

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: 3 November 2022