FIE463 Numerical methods in Macroeconomics and Finance using Python

• Topics

  FIE463 introduces students to Python programming with applications focused on topics in macroeconomics and finance. Over the past decade, Python has become both the most dominant general-purpose programming language as well as the most widely used for (data) science and machine learning, where it has been embraced by all major industry players such as Google, Meta, and OpenAI. This course aims to provide students with a solid working knowledge of Python and its ecosystem, a skill that is increasingly valued in the finance industry and beyond.

  This course is split into three modules:

  1. Introduction to Python programming fundamentals, core libraries, and tools.
  2. Application to questions in macroeconomics & finance such as consumption/savings/portfolio choice problems, analysis of income and wealth inequality, and simple asset pricing.
  3. Working with and analyzing macroeconomic & financial data.

  The course additionally emphasizes the use of industry-standard professional development tools and techniques such as version control (git), and integrated development environments such (Visual Studio Code or PyCharm).

  The course can be taken with little or no knowledge of Python. Prior knowledge of other programming language such as R, Matlab or Julia is advantageous as part (1) will proceed at a quick pace.

• Learning outcome

  Upon completion of the course, the student can…

  Knowledge

  • Identify fundamental programming concepts that are applicable to a given problem.
  • Implement macroeconomic & finance problems in an efficient, reproducible way.

  Skills

  • Use standard tools for developing Python applications (Visual Studio Code or PyCharm, Anaconda, Jupyter, git version control & GitHub).
  • Leverage the functionality provided by standard libraries such as NumPy, SciPy and scikit-learn to effectively write their own applications.
  • Solve common problems in macroeconomics and finance using Python.
  • Critically reflect on the importance of assumptions when interpreting model predictions.
  • Import, process and analyze data in various formats coming from various sources.
  • Visualize data and results using standard libraries.

  General competence

  • Employ online documentation to effectively leverage existing libraries.
  • Use distributed version control systems for collaboration.
  • Solve programming problems in teams.

• Teaching

  This course is taught using a combination of hands-on lectures to develop new concepts and weekly workshops in which students are asked to apply these concepts to solve problems in Python on their own laptops.

• Recommended prerequisites

  Prior experience with either Python or programming languages such as R, Matlab or Julia is helpful.

• Compulsory Activity

  Students have to individually complete one assignment given during the first part of the course for course approval.

• Assessment

  The assessment in FIE463 consists of four parts:

  1. Group term paper handed out in middle of the semester focusing on parts 1+2 (to be completed within one week in groups of 2-3 students) [40%]

  2. Group term paper handed out in the last week of teaching focusing on part 3 (to be completed within two weeks in groups of 2-3 students) [50%]

  3. Peer review of another group’s first term paper (individual) [5%]

  4. Peer review of another group’s second term paper (individual) [5%]

  The peer reviews are intended to give students additional feedback on code style, structure and efficiency in a respectful, constructive manner.

  All components must be answered in English.

• Grading Scale

  A-F.

• Computer tools

  Students are expected to install Python and all required tools on their own laptops (running Windows, macOS or Linux).

• Literature

  Course responsible will create all course material, the students do not need to access any papers or books.