Autumn 2019Spring 2020
Topics will be covered in the following sequence.
- General overview: Microdata
- General overview: Causes and effects
- Recent advances in empirical methods
- Critical review of a few selected working papers
After completion of the course, the candidate should be able to:
- be familiar with key sources of high-quality microdata
- critically read and comprehend relevant scientific papers addressing causal inference and identification
- formulate identification challenges and propose solution procedures to these challenges using different types of instrumental variables
- be familiar with the most recent literature within a pre-defined topic
- formulate a research question
- be explicit about the research design needed to answer the research question and discuss its strengths and weaknesses
- implement and use empirical methods in software like R, Julia or Python
Topics will be lectured over three days with theory in the morning session and practical exercises/discussions in the evening session.
The first meeting covers the basics, and we will agree upon the topic/papers that we will cover during the last meeting.
Before the second meeting, I will ask you to replicate a part of the methodological paper, which I will cover in class.
In the last meeting, I will cover a few working papers we agree upon in the first meeting, and you are expected to suggest how these papers can be extended.
The final exam will be based on turning these ideas into a research proposal.
Some prior experience working with data is useful, but not required.
Knowledge of basic statistics and programming.
Requirements for course approval
1 assignment during the course (replication)
Final individual term paper (research proposal).
Pass / Fail
R, Julia, Matlab, Python, etc.
All topics in the course are covered by scientific papers and selected parts in advanced textbooks. The course material is given as handouts and web links.
- ECTS Credits
- Teaching language
Spring, offered sping 2020
Adjunct Associate Professor Jens Sørlie Kværner, Department of Business and Management Science