Duration
30h Th, 15h Pr
Number of credits
| Master in economics : general (120 ECTS) | 5 crédits |
Lecturer
Substitute(s)
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This is a graduate course in applied time series econometrics. This course takes an inductive perspective to develop a significant understanding of the role of time series econometrics in empirical economics and a strong ability to execute applied time series econometrics in the development of economic models. Identification, estimation, evaluation, hypothesis testing, and forecasting will be emphasized.
The main problems which can be encountered in econometric modeling with macroeconomic time series will be first introduced, and practical examples will be given. Then, all the basic notions concerning time series will be addressed in a univariate framework. Formal examples as well as practical illustration on real financial and macroeconomic data will be given. Finally, the course ends by an introduction to the multivariate framework. If time allows further useful methods but more recent methods for time series analysis will be covered, such as the synthetic control and maching learning methods.
Learning outcomes of the learning unit
At the end of the course the student is expected to be able to
Knowledge and understanding:
- Explain and describe the main statistical methods for time series analysis.
- Implement both univariate and multivariate time series models
- Select most appropriate model
- Test for non stationarity
- Modeling non stationary time series with trends or volatility
- Use of a programming and statistical software: python and main python functions for time series analysis
- Apply and properly interpret the models and methods presented in the course in applications.
- Evaluate and justify their analysis on real data.
- Prepare appropriate reports of their statistical analysis in real data applications.
Prerequisite knowledge and skills
Students should be comfortable with undergraduate microeconomics, statistics and econometrics classes.
Planned learning activities and teaching methods
- Lectures introducing the concepts
- Tutorials: computer labs and pen-and-paper exercises
Mode of delivery (face to face, distance learning, hybrid learning)
Recommended or required readings
- Enders, W Applied Econometric Time Series. Wiley Global Education US, 2014.
- Hyndman, R.J., Athanasopoulos, G. Forecasting: principles and practice. 3rd edition, OTexts: Melbourne, Australia, 2021.
- Stock J, Watson MW. Introduction to Econometrics. New York: Prentice Hall, 2003.
Exam(s) in session
Any session
- In-person
written exam ( multiple-choice questionnaire, open-ended questions )
Written work / report
Continuous assessment
Out-of-session test(s)
Additional information:
Course project: 20%
- Handed in at the end of the year, valid for both sessions
- Handed in during the year, valid for both sessions
Work placement(s)
Organisational remarks and main changes to the course
Contacts
- Lecturer: Malka Guillot (mguillot@uliege.be ) and Maren Ulm (m.ulm@uliege.be)
- Assistant: Laura Heymans