Econometrics and Machine Learning (MSB145)
In the increasingly data-driven business environment, it is crucial for a modern econ and finance graduate to know how to use data. This course offers students a comprehensive exploration of the fundamental principles of econometrics and machine learning, with a specific focus on their applications in finance and economics. By examining the core concepts of both disciplines, students will gain a deep understanding of the strengths and limitations of each, enabling them to make informed choices when addressing real-world problems. At the end of the course, students will possess a versatile skill set, allowing them to navigate complex issues in finance and economics, making data-driven decisions while appreciating the nuances of both econometric and machine learning methodologies.
Course description for study year 2024-2025. Please note that changes may occur.
Course code
MSB145
Version
1
Credits (ECTS)
10
Semester tution start
Spring
Number of semesters
1
Exam semester
Spring
Language of instruction
English
Content
Examples of typical subject areas covered are:
- Multiple Linear Regression
- Endogeneity Bias
- Randomized Controlled Trials
- Regression Discontinuity Design
- Instrumental Variable Regression
- Panel Data Estimation
- Differences-in-Differences
- Time series
- Forecasting
- Machine Learning Methods
Learning outcome
Knowledge
On completion of the course, students will gain knowledge in:
- Econometric Methods
- Machine Learning Methods
- Advanced programming in R
Skills
Upon completion of this course, students will be able to:
- Interpret the results of different econometric and machine learning methods.
- Implement econometric and machine learning methods in new data analysis contexts.
- Compare and contrast different econometric and machine learning methods to answer a research question with data.
- Formulate a research question and analyze it with data and the methods learned using R.
- Show skills for written communication and use of artificial intelligence tools.
- Demonstrate abilities to communicate and work effectively with others.
Required prerequisite knowledge
Exam
Portfolio and group presentation
| Form of assessment | Weight | Duration | Marks | Aid |
|---|---|---|---|---|
| Portfolio | 8/10 | Letter grades | ||
| Group presentation | 2/10 | Letter grades |
Coursework requirements
Course teacher(s)
Course coordinator:
Simone Valerie Häckl-SchermerCourse teacher:
Eric Perry BettingerStudy Program Director:
Yuko OnozakaMethod of work
This course uses a mixture of interactive lectures, TA sessions, and individual study. Lecture slides
provide the basic concepts. The material is explained and extended in the in-person lectures which also give room for student questions. Programming and empirical exercises are discussed in the TA sessions.