Identification

BEA523 Identification

Autumn 2019

Spring 2020
  • 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.

  • Recommended prerequisites

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

  • Required prerequisites

    Knowledge of basic statistics and programming.

  • Requirements for course approval

    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.

Overview

ECTS Credits
5
Teaching language
English
Semester

Spring, offered sping 2020

Course responsible

Adjunct Associate Professor Jens Sørlie Kværner, Department of Business and Management Science