Business and Financial Forecasting

Summary module description:
Introduction to forecasting and to R
Non-stationarity and structural breaks
Judgemental and Regression-based Forecasts
Structural Time Series Modelling
Exponential Smoothing Methods
ARIMA Models
Practical introduction to R (weeks 5-8)
Project guidance and ideas (weeks 9-11)

Aims:
To convey a practical understanding and ability to forecast economic time series.
To develop skills related to the modelling of economic time series using econometric software.
To write a project report combining economic and forecasting analysis.

Assessable learning outcomes:
1. Knowledge/Comprehension: Be aware of forecasts, their potential and limitations, their basic properties and how to evaluate them.
2. Application/Analysis: Familiarity with various types of forecast models and approaches, ability to appraise their strengths and weaknesses, and practically construct each type of forecast.
3. Synthesis/Evaluation: Ability to identify an economic variable to forecast, construct the optimal model, and describe the results.

Additional outcomes:
Proficiency in the use of statistical software for forecasting purposes.
Ability to display and analyse graphical data.
Ability to write a project report.

Outline content:
The analysis of economic data: graphical representation.
An assessment of forecasting models, especially unobservable components models.
A guide to econometric software for forecasting purposes.
Models for trend, seasonality, cyclical and irregular components.
The construction and evaluation of ARMA and other models for forecasting.
Evaluating forecasting accuracy.
Applications: examples of the construction of forecasting models.
Project work involving the analysis of ?real? economic data.

Brief description of teaching and learning methods:
Formal lectures, practical classes and tutorials; practical implementation via computer classes; supported independent study.

Summative Assessment Methods:
Project output other than dissertation 40%
Oral assessment and presentation 10%
Class test administered by School 50%

Other information on summative assessment:
? Mid-term test 1 will occur in week 4 and will be based on material covered in weeks 1-3.
? Mid-term test 2 will occur in week 8 and will be based on material covered in weeks 4-5.
? Project topics will be determined in consultation with module lecturer before week 9 of spring term. A data series must be chosen for which some future values will be released/realised after the coursework submission deadline.
? Presentations occur at the end of the spring term in week 11. It is a 10-minute presentation on the proposed project, with a pilot forecast presented.

Formative assessment methods:
Each week students will participate in a forecast competition, and also submit an online questionnaire which will include one short progress question. Answers to these questions will not count towards the final grade but will enable subsequent course content to be amended if need be to address identified needs.

Penalties for late submission:
The Module Convener will apply the following penalties for work submitted late, in accordance with the University policy.
where the piece of work is submitted up to one calendar week after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for the piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

The University policy statement on penalties for late submission can be found at: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf
You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Requirements for a pass:
A minimum overall mark of 40%.