Forecasting

Recommended Prerequisites

Students attending this module should be familiar with basic economic, statistical and econometric terminology. Basic knowledge of statistics and econometrics is desirable. Familiarity with software (in special spreadsheets and econometrics packages) will be useful during classes and for solving exercises.

Teaching Methods

To acquire specific competencies: classes with both a lecture and a tutorial component; class discussion of forecasting exercises; homework; student forecasting projects. To develop generic competencies there is a periodic evaluation regime. This regime includes, besides the test (for which students should prepare by working on the exercises given, both during the classes and at home), writing and presenting a forecasting project.  

Learning Outcomes

By the end of this unit, the student is expected to be able to:

1) Explain the need for knowing the source, the meaning and the characteristics of the data.

2) Suggest and apply adequate transformations to the data.

3) Implement different forecasting methods.

4) Explain the advantages and disadvantages of different forecasting methods.

5) Select, in face of the forecasting context, adequate forecasting methods.

 

Competencies:

1) Specific:

- Collect, analyze and treat the data required in the forecasting process.

- Present different forecasting methods, discussing their advantages and disadvantages.

- Use software to build and analyze forecasting models.

2) Generic:

- Analyze data.

- Solve problems.

- Use computer programs.

- Write and present reports.

- Plan work activities.

- Make decisions.

- Work in a team. 

Work Placement(s)

No

Syllabus

1. Introduction to the forecasting process

2. Decomposition of economic time series

3. Exponential smoothing

4. Forecast evaluation

5. Forecasting trending series

6. Forecasting with breaks

7. Forecast combination.

Assessment Methods

Assessment
Periodic or by final exam as given in the course information: 100.0%

Bibliography

Armstrong, J.S. (ed.) (2002). Principles of Forecasting: A Handbook for Researchers and Practitioners. Springer.

Box, G.E.P.; Jenkins, G.M.; Reinsel, G.C. (2015). Time Series Analysis: Forecasting and Control, 5th ed. Wiley-Blackwell.

Brockwell, P.J.; Davis, R.A. (2016). Introduction to Time Series and Forecasting. Springer.

Diebold, F.X. (2017). Forecasting. Department of Economics, University of Pennsylvania. http://www.ssc.upenn.edu/~fdiebold/Textbooks.html

Elliott, G.; Timmermann, A. (2016). Economic Forecasting. Princeton University Press.

Ghysels, E.; Marcellino, M. (2018). Applied Economic Forecasting using Time Series Methods. Oxford University Press.

Hyndman, R.J.; Athanasopoulos, G. (2021). Forecasting: principles and practice, 3rd ed. OTexts.

Kuhn, M.; Johnson, K. (2013). Applied Predictive Modeling. Springer.

Tetlock, P.; Gardner, D. (2015). Superforecasting: The Art and Science of Prediction. Random House Books.