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This is the study programme for 2020/2021.


In most of economics, marketing and business management, we are interested in causal relations between variables, rather than mere correlations. For example, it is not the correlation between marketing expenses and sales that is of interest, but the effect of increasing marketing expenses for a product on the sale volume of the same product.

Learning outcome

Knowledge
Students should be conversant about methods for estimating and identifying causal effects, and have the intuition and skills necessary to design and implement empirical strategies for causal analysis.

Skills
Students should be able to
  • Understand causal modeling
  • Gain the statistical tools for causal analysis
  • Design and implement empirical strategies for causal analysis
  • Become aware of underlying assumptions, strengths, and weaknesses of different experimental and quasi experimental approaches
  • Become acquainted with key players in the development and implementation of strategies for causal analysis

General competence
The student should be conversant in the world of causal analyses, using instrumental variables, difference-in-difference, regression discontinuity design, synthetic control methods, and randomized controlled trials. They should be able to be consumers and producers of causal analyses. They should have the background to develop and implement ethically sound and scientifically valid empirical strategies. They should also have proficiency in software designed to implement causal analysis.

Contents

In this course, we study methods for estimating and identifying such causal effects. First, we discuss randomized experiments as the predominant way for establishing causality. Second, we discuss designs and methods for causal analysis using observational data. In particular, designs and methods covered include instrumental variables, fixed effects and panel data, difference-in-difference, regression discontinuity design, and synthetic control methods.

Required prerequisite knowledge

The course is open to all PhD students who have taken courses in statistics or econometric which include regression analyses.

Recommended previous knowledge

General PhD statistics courses including regression analysis or econometrics courses would be an advantage.

Exam

Weight Duration Marks Aid
Individual essay1/1 Pass - Fail
Student evaluation will be conducted according to the regulations set forth by the Faculty of Social sciences.

Coursework requirements

  • Full participation during the whole course is required. (One week on intensive lectures and PC-lab exercises).
  • Active participation in discussions in general.
  • In addition, there is a term paper requirement ( 8-10 pages, demonstrating knowledge and application skills).

Course teacher(s)

Course teacher
Yuko Onozaka
Course coordinator
Ragnar Tveterås , Mari Rege

Method of work

The course will be taught through a combination of lectures, seminars and exercises. The seminars will include student presentations and discussions. Each student will be required to give one seminar presentation. All students are expected to read required literature ahead of the seminars and to participate actively in the discussions. Exercises will be undertaken on empirical cases (firms, sectors, markets) using appropriate data and software tools. Students will also have a written assignment in addition to the final essay.

Estimated student workload in hours:
1. Lecture 28
2. Seminar/lab exercise 8
3. Specific supervision 20
4. Student's self studies 50
5. Written assignment 40
TOTAL 146

Literature


Reading List


This is the study programme for 2020/2021.

Sist oppdatert: 05.07.2020