|
| Module Availability |
| Spring |
|
|
| Assessment Pattern |
| Unit(s) of Assessment |
Weighting Towards Module
Mark (%)
|
| 2 hour Examination |
70
|
| Coursework |
30
|
Qualifying Condition(s)
A weighted aggregated mark of 40% is required to pass the module |
|
|
|
| Module Overview |
Introduction to economic forecasting. The module covers the following topics: forecasting the trend, ARMA models of the cycle, modelling seasonality, forecasting with macroeconomic models, assessing forecasts and smoothing methods |
|
|
| Prerequisites/Co-requisites |
| None |
|
|
| Module Aims |
To examine the priciples and the practice of making forecasts to aid decisions, develop the relevant techniques and use appropriate software to build forecasting models and make and assess forecasts |
|
|
| Learning Outcomes |
By the end of the module students should:-
- be able to identify appropriate forecasting models for a variety of situations
- be proficient in the application of different forecasting methods
- understand the statistical properties of ARIMA and macroeconomic forecasting models
- be aware of the limitations of forecast results
|
|
|
| Module Content |
- deterministic and stochastic trends and cycles
- identification, estimation and forecasting with ARMA models
- modelling seasonality
- forecasting with macroeconomic models
- assessing and combining forecasts
- smoothing methods
|
|
|
| Methods of Teaching/Learning |
| Lectures (11) |
|
|
| Selected Texts/Journals |
Diebold, F.X, Elements of Forecasting, latest edition, Thomson, South-Western |
|
|
| Last Updated |
10 March 2011 |
|
