Description of the course
This is a course in research design. A research design is the qualitative story that is going to give you a causal interpretation of the results of your quantitative analysis. We focus on evaluation of public policy / business strategy. We assume that you have an understanding of basic econometrics; successful completion of an introductory course in econometrics is required.
During the course, after introducing the fundamental problem of causal inference - and causal diagrams as a tool to visualize identification problems - we discuss four commonly used research designs: randomized trial, regression discontinuity, difference-in-difference, instrumental variable.
The course should help you to conduct meaningful and creative empirical work by yourself, by learning to recognize research designs in the day to day world. In addition, this course should help you to assess the quality of empirical work from others.
For the exercises, we will work with R and RStudio.
Introductory course in econometrics and familiarity with the linear regression model, based on a textbook like the 'baby Wooldridge' or Stock and Watson.
Type of instructions
Interactive lectures and computer labs (5 weeks in total)
Type of exams
Final exam, weekly quizzes, weekly paper reviews, two weekly computer assignments.
- Joshua Angrist and Jörn-Steffen Pischke, Mastering Metrics. The path from cause to effect, Princeton University Press, 2015. (available from last year's students)
- Jeffrey M. Wooldridge, Introductory Econometrics. A Modern Approach, South-Western. CENGAGE Learning, 2013.