Business and Financial Forecasting

Module code: EC7105
Module co-ordinator: Professor Stephen Hall

Financial risk management is about understanding and forecasting the future as well as understanding the risks attached to these forecasts. In this module you will study the fundamental concepts and techniques used in forecasting economic and financial series and gain practical experience of a range of forecasting procedures from quite simple univariate processes to advanced systems.

We will look at the basic building blocks of forecasting; the autoregressive and moving average models, Box Jenkins analysis and a range of well-known simple forecasting models. We will then consider a range of techniques for smoothing data including the Hoderick-Prescott filter and the structural time series model using the Kalman Filter. Then we will explore recent advances in time series analysis including co-integration and vector error correction models and the methods used to correctly identify economic structures within this framework.

We will look at the literature dealing with the analysis of uncertainty and examine the concept of conditional volatility and how to model such processes in a univariate and system setting. Finally you will explore the use of Monte Carlo analysis in the understanding of risk and uncertainty.

During this module you will make extensive use of practical examples and applications using the EVIEWS software package.

Topics covered

  • Time series
  • Techniques for smoothing data and identifying and testing time series models
  • Derive optimal forecasts for appropriate business and economic problems
  • Evaluation of estimated models and forecasts


  • 20 one-hour lectures
  • 5 hours of practical classes and workshops
  • 125 hours of guided independent study


  • Exam, one hour (30%)
  • Individual project (70%)