BEA523 Causes and Effects in Economics and Management Science (not offered)

• Topics

  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

• Learning outcome

  After completion of the course, the candidate should be able to:

  Knowledge

  • 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

  Skills

  • 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

• Teaching

  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.

  If necessary, depending on the Corona situation the course will be digital.

• Restricted access

  Open to all candidates in a (Norwegian) PhD program

• Recommended prerequisites

  Some prior experience working with data is useful, but not required.

• Required prerequisites

  Knowledge of basic statistics and programming.

• Compulsory Activity

  1 assignment during the course (replication)

• Assessment

  Final individual term paper (research proposal).

• Grading Scale

  Pass / Fail

• Computer tools

  R, Julia, Matlab, Python, etc.

• Literature

  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.