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.

Facts

Course code

MSB145

Version

1

Credits (ECTS)

10

Semester tution start

Spring

Number of semesters

1

Exam semester

Spring

Language of instruction

English

Offered by

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

Undergraduate level statistics (e.g. BØK356).

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

Quizzes, Assignments

Course teacher(s)

Course coordinator:

Simone Valerie Häckl-Schermer

Course teacher:

Eric Perry Bettinger

Study Program Director:

Yuko Onozaka

Method 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.

Course assessment

There must be an early dialogue between the course supervisor, the student union representative and the students. The purpose is feedback from the students for changes and adjustments in the course for the current semester.In addition, a digital subject evaluation must be carried out at least every three years. Its purpose is to gather the students experiences with the course.

Literature

Search for literature in Leganto