Course

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.


Dette er emnebeskrivelsen for studieåret 2024-2025. Merk at det kan komme endringer.

See course description and exam/assesment information for this semester (2024-2025)

Semesters

Fakta

Emnekode

MSB145

Vekting (stp)

10

Semester undervisningsstart

Spring

Undervisningsspråk

English

Antall semestre

1

Vurderingssemester

Spring

Timeplan

Vis timeplan

Litteratur

Søk etter pensumlitteratur i Leganto

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.

Forkunnskapskrav

Undergraduate level statistics (e.g. BØK356).

Exam

Form of assessment Weight Duration Marks Aid Exam system Withdrawal deadline Exam date
Portfolio 8/10 Letter grades Inspera assessment — —
Group presentation 2/10 Letter grades — — —


Vilkår for å gå opp til eksamen/vurdering

Quizzes, Assignments

Fagperson(er)

Study Program Director:

Ingeborg Foldøy Solli

Course teacher:

Eric Perry Bettinger

Course teacher:

Peter Molnar

Course coordinator:

Simone Valerie Häckl-Schermer

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.

Åpent for

Master of Science in Accounting and Auditing Business Administration - Master of Science

Emneevaluering

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.

Kontakt

Study Program Director:

Ingeborg Foldøy Solli

Course teacher:

Eric Perry Bettinger

Course teacher:

Peter Molnar

Course coordinator:

Simone Valerie Häckl-Schermer

Tilbys av

UiS Business School

UiS School of Business and Law

The course description is retrieved from FS (Felles studentsystem). Version 1