EBA 3650 Quantitative Economics

EBA 3650 Quantitative Economics

Course code: 
EBA 3650
Course coordinator: 
Christian Brinch
Course name in Norwegian: 
Quantitative Economics
Product category: 
Bachelor of Data Science for Business - Programme Courses
2023 Spring
Active status: 
Level of study: 
Teaching language: 
Course type: 
One semester

This course teaches the application of digital techniques for solving models in economics based using numerical software, primarily Python. The course has the dual aim of teaching how to use digital tools for solving economic models and to apply these techniques to microeconomic models that are relevant for optimal decision making in firms.

Learning outcomes - Knowledge

After taking this course the student:

  • Knows how to apply microeconomic modeling to firm decision making
  • Knows how to turn a simple economic model into a mathematic model that can be solved numerically
  • Knows a handful of useful mathematical techniques for numerical optimization and equation solving
Learning outcomes - Skills

After taking this course the student:

  • Can practically apply simple general numerical tools for optimization and equation solving,
  • Can numerically solve various constrained optimization problems, such as finding the optimal price a firm can specify for a product, given a known demand function.
  • Can numerically solve for equilibrium solutions resulting from several agents’ optimizing decisions, such as finding a market price in a market with known supply and demand functions – and to study how factors that affect demand or supply affects the market price.
  • Can numerically solve for optimal decisions and equilibrium solutions in game-theoretic models with strategic interaction, such as models of oligopoly.
  • Can numerically quantify the welfare loss resulting from pricing decisions that exploits market power.
General Competence

After taking this course the student:

  • Has gained an understanding of how one can practically apply numerical techniques to quantity the welfare loss caused by the exercise of market power
  • Has understood how one can improve the understanding of economic models and problems by specifying models numerically, experimenting with different specifications and studying the properties of the models graphically and numerically
Course content

We start the course with a recap of Python and how we can use Python to solve problems in mathematics as well to produce graphs.

We then go through different topics in Business Economics. For each topic, we go briefly through the economic content of the topics with an emphasis of the explicit models used. We then proceed to implement some of these models numerically and to analyse the models using numerical techniques.

The main substantive topics covered are:

  • The market system and the limitation of markets.
  • The economics of firms in markets.
  • Factor markets.
  • Economic analysis with forward looking agents.

In the final part of the course, students form groups and perform in-depth analyses of economic / business problems using the techniques taught in the course. Some emphasis is put on presenting analyses in an accessible way.

Teaching and learning activities

There will be 12 lectures of 3 hours, where about half of the lecture time will be used for problem solving in groups in class, under supervision.

At the end of the term, the students will write reports in groups based ib a topic chosen by the group under supervision of the lecturer. There will also be some supervision of the groups during the initial phases of the report period.

Software tools
Software defined under the section "Teaching and learning activities".
Additional information

We will primarily use Python.


Higher Education Entrance Qualification


Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.

Exam category: 
Form of assessment: 
Written submission
Group (3 - 4)
4 Week(s)
This report is an exposition of a numerical analysis of an economic model applied to a business problem.
Exam code: 
EBA 36501
Grading scale: 
Point scale leading to ECTS letter grade
All components must, as a main rule, be retaken during next scheduled course
Exam category: 
Form of assessment: 
Oral examination
Group/Individual (3 - 4)
45 Minute(s)
This is a group presentation of the handed-in report, with an opening for the examiner to ask individual questions. The group distributes tasks and presents the work they have done, the examiner asks questions including control questions to individuals to reveal that everyone has contributed and understands what they have done.
Exam code: 
EBA 36501
Grading scale: 
Point scale leading to ECTS letter grade
All components must, as a main rule, be retaken during next scheduled course
Type of Assessment: 
Continuous assessment
Grading scale: 
Total weight: 
Student workload
36 Hour(s)
Lectures and supervised group work
Prepare for teaching
46 Hour(s)
Group work / Assignments
58 Hour(s)
60 Hour(s)
Written assignment and oral exam
Sum workload: 

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 7,5 ECTS credit corresponds to a workload of at least 200 hours.