Forecasting and Simulations 2400-M1IiEPiS
General Topics
1. Introduction to forecasting and simulations
2. Uncertainty, error, and quality of forecasts and simulations
3. Key problems and practical Issues in forecasting and simulations
Review of selected methods and models in the context of forecasting and simulations
4. Simple time series forecasting methods and models
5. ARIMA models
6. ADL and ARIMAX models
7. Structural and semistructural models
8. VAR models
9. General equilibrium models
10. The indicator approach (based on the example of economic forecasts)
Estimated student workload
(C) - Contact hours (I) - Independent work hours
4 ECTS = 100 hours
In-person classes: 30 hours (C)
Work on final projects: 50 hours (S)
Exam preparation: 15 hours (C)
Consultations: 5 hours (C)
Total: 30 hours (C) + 50 hours (C) + 15 hours (C) + 5 hours (C) = 100 hours
Type of course
Prerequisites (description)
Course coordinators
Learning outcomes
Learning outcomes (codes): K_W01, K_W02, K_W03, K_W04, K_U01, K_U02, K_U04, K_K01, K_K03.
After completing the course, the student:
KNOWLEDGE
- has basic theoretical and practical knowledge of the discussed models, methods and techniques of forecasting and simulations
- has deeper theoretical and practical knowledge of the models, methods and techniques of forecasting and simulations used in final projects
- knows the economic and econometric context of the discussed models, methods and techniques of forecasting and simulations
- knows examples of applications of the discussed models, methods and techniques of forecasting and simulations
- knows which statistical and econometric environment can be used to apply the discussed models, methods and techniques of forecasting and simulations in practice
SKILLS
- is able to read and interpret the results of the discussed models, methods and forecasting and simulation techniques
- is able to create forecasts and perform simulations
based on the models, methods and forecasting and simulation techniques used in their own projects
- is able to read and understand empirical studies using the discussed models, methods and forecasting and simulation techniques
SOCIAL COMPETENCES
- understands the need to conduct scientific research and publish its results
- complies with ethical standards of scientific work and publication
- demonstrates a willingness to expand their knowledge and skills
- demonstrates a willingness to work independently or in a group of two
Assessment criteria
2/3 – final projects (own work on real data)
1/3 – written exam
To pass the course, students must obtain at least 50% on both the final projects and the exam.
Exemption from the exam with a final grade 5 is possible for at least two students with the highest scores on the final projects.
Possibility of earning additional credits based on forecasting competitions.
Bibliography
- Materials provided by the lecturer
- Clements M., D. Hendry (1999): Forecasting economic time series, Cambridge University Press.
- Hendry D., Castle J., Clements M. (2019): Forecasting: An Essential Introduction, Yale University Press.
- Hyndman R., Athanasopoulos G. (2018): Forecasting: principles and practice, OTexts.
- Tetlock P. E., Gardner D. (2017): Superprognozowanie. Sztuka i nauka prognozowania, CeDeWu.
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: