GRA 6612 Analysis of Experiments and Quasi-Experiments
GRA 6612 Analysis of Experiments and Quasi-Experiments
This course teaches modern design based approaches to empirical analysis that are prevalent in applied microeconomics. In particular, we focus on analysis techniques associated with credible empirical evidence - with a strong leaning towards experimental and quasi-experimental techniques. The course teaches both the actual implementation of the techniques using statistical software and relevant applications in the economics literature, with a particular emphasis on business relevant topics, such as personnel economics and industrial organisation, in addition to classical applications in labor economics, health economics and development economics. We also explore to what extent the analysis techniques are used and can be used in business contexts.
After taking this course, students should
- know how to analyze empirical microeconomic problems using data from lab experiments or field experiments,
- know how to analyze data using regression techniques to generate comparable samples,
- know how to analyze data using instrumental variable techniques to overcome exogeneity problems,
- know how to exploit regression discontinuity designs,
- know how to analyze data using the difference-in-differences methodology.
After taking this course, students should
- be able to perform experimental and quasi-experimental analyses of economic data using statistical software
- be able to assess empirical analyses of economic problems, evaluating to what extent the analyses provide credible empirical evidence.
After taking this course, students should
- understand the selection problem and why correlation does not imply causality
- understand how and why randomized experiments enable researchers to answer causal questions empirically and why observational studies only rarely enable researchers to do provide such answers
- Empirical strategies in applied microeconomics
- Comparing experimental techniques and observational studies
- Implementation of empirical strategies in statistical software
- Key scientific studies in business relevant economics
- Applications of experimental techniques to business problems
Continuous assessment will no longer exist as an examination form from autumn 2023. For questions regarding previous results, contact InfoHub.
It is the student’s own responsibility to obtain any information provided in class.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
| Assessments |
|---|
Exam category: Submission Form of assessment: Handin - all file types Weight: 30 Grouping: Group/Individual (2 - 4) Duration: 6 Week(s) Comment: Presentation (video) - 45 minutes Exam code: GRA 66122 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 70 Grouping: Individual Duration: 5 Hour(s) Comment: Home exam Exam code: GRA 66123 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 Hour(s) | |
Examination | 3 Hour(s) | |
Group work / Assignments | 8 Hour(s) | Preparations for student presentation |
Student's own work with learning resources | 113 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.