Duration
30h Th
Number of credits
| Doctoral training in economics and business management (Management) | 5 crédits |
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the first semester, review in January
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
The world of management shows a deeper and deeper interest for quantitative forecasting methods (data science). For the broker, having good approximations of future values of his equity portfolio is essential. An economist or business analyst should be able to explore and simulate different possible futures under alternative scenarios, assessing their implications for relevant economic variables and indicators. This may involve forecasting the evolution of demand, prices, income, or employment, as well as evaluating the potential effects of changes in economic policy, market conditions, or agents' behaviour.. In this framework, this course develops different existing methods to treat those problems. Its content heavily depends on students' interests and their professional expectations. Among others, topics in the sequel can be involved.
- Forecasting of seasonal data
- Risk management
- Causality
- Autoregressive moving average models (ARMA models)
- Generalized autoregressive conditional heteroscedasticity models (GARCH models)
- ...
Numerous methods nowadays rely on machine learning and, more broadly, on artificial intelligence.
Learning outcomes of the learning unit
P2. Application of basic data science methods to different kinds of data
C3. Analysis, identification of common denominators in the different methods
C4. Critical analysis of existing methods and their results with respect to practical situations
These learning objectives are part of and precise the following more general Intended Learning Outcomes of the Business School:
- Understanding and being capable of using modelization methods when seeking a solution for a concrete management/economics problem
- Being capable of professional team work
- Developing a critical sense (arguing)
- Professional capacity for oral communication
Prerequisite knowledge and skills
1) Basic course in probability (cumulative distribution function, density, distribution, mean, variance, usual discrete and continuous univariate laws, multivariate normal) and statistical inference (estimation , confidence intervals, hypothesis tests). Equivalent to the content of the course: Probability and statistical inference STAT1208-1.
2) Course of quantitative methods in management: mainly multiple regression, maximum likelihood estimation and principal component analysis. For example, this content is studied in
STAT0800-1 Models and Methods in Applied Statistics, or
MQGE0005 Quantitative Methods in Management (Partim Statistics).
Planned learning activities and teaching methods
Mode of delivery (face to face, distance learning, hybrid learning)
Blended learning
Further information:
Used methodology
A3. Analysis of a practical problem by each group of students (partially followed up by the teacher).
A4. Critical synthesis of searches, readings and/or practical applications achieved by each group of students. Each student presents his own results and then, each group discusses, compares the different methods and presents other possible obtained results.
During his talk, each group is invited to
1) clearly present the problem of interest in its context and the existing methods to solve it,
2) discuss those methods and justify the choice of one or several of them in specific cases.
Overview of the course agenda
The first weeks, the teacher presents the different problems of interest with the necessary corresponding theoretical basic knowledge. Then, students (in groups or indiviually) receive a problem and try to understand it, on one side, with a personal bibliographic search (in agreement with their potential group -shared bibliographic search-) and on the other side, with the courses and/or discussions conducted the weeks after. After some weeks, the students and the teacher meet to assess the progress of the work and define the remaining steps to achieve. Finally, the students prepare an oral presentation of their problem and write a report for the evaluations period that follows the course.
Course materials and recommended or required readings
Platform(s) used for course materials:
- LOL@
Further information:
Advised readings:
1. Franses, P. H. (1998). Time series models for business and economic forecasting. Cambridge University Press.
2. James G., Witten D., Hastie T. and Tibshirani R. (2013), An Introduction to Statistical Learning with Applications in R, Springer.
3. Mills, T. C. (1999). The Econometric Modelling of Financial Time Series (Second ed.). Cambridge University Press.
4. Auffarth, B. (2021). Machine Learning for Time Series with Python. Packt Publishing Ltd.
5. Advised readings (according to each student)
Written work / report
Continuous assessment
Out-of-session test(s)
Further information:
Evaluation tools, evaluation criterions and weighting
E4. Final report (20% of the final note, common evaluation)
The evaluation is based on the ability to clearly synthesize and criticize the results.
E4. Oral presentation (70% of the final note, 10% common)
1. Quality of the presentation:
quality of the slides (10%, common),
scientific methodology (20%) and quality of the explanations (20%).
Each student identifies and presents its own work.
2. Defence of the work: answers to the questions of the teacher (20%).
E4. Attending and discussing issues during the course, quality of asked questions (10%, individual evaluation)
Relative weighting of individual assessment: 70%
Evaluations agenda
The final report has to be sent to the teacher before the evaluations period that follows the course. The oral presentation is usually held if possible during the courses period.
Work placement(s)
Organisational remarks and main changes to the course
Teaching language: English
Contacts
Cédric HEUCHENNE, HEC Liège, N1, local 309, email: C.Heuchenne@uliege.be