ECS509 PhD Econometrics II
This is the second of the two courses in econometrics in the PhD program in economics. The goal of the course is to make students familiar with econometric techniques at an advanced level. The course provides a deeper understanding of modern econometric methods that are applied in many fields of economics. The course also puts strong emphasis on practical empirical exercises. The students train at critically assessing studies, but also in doing advanced empirical studies with data that are provided in class. We cover identification, randomization, causal inference, instrumental variables, difference-in-differences, regression discontinuity, and matching methods. We also cover a number of more specialized applied topics such as bad controls, measurement error, and clustering.
Upon completion of the course, the students will be able to:
- Identify and understand [explain or discuss?] the standard identification strategies in research papers
- Calculate standard estimators and corresponding standard errors
- Discuss inferential issues with dependent data
- Use available software to estimate standard models on real world data
- Evaluate alternative identification strategies
Lectures and assignments. Due to the current pandemic, the lectures will be filmed and/or streamed.
Requirements for course approval
Approved referee assignment and class participation.
Group-based replication project.
Group size: 2.
Scope: 4000 words maximum.
The assignment will be distributed in January. The submission deadline will be in April.
In addition to selected papers provided on canvas, there is one textbook:
Angrist and Pischke (2009). Mostly Harmless Econometrics: An Empiricist's Companion, Princeton, NJ: Princeton University.
- ECTS Credits
- Teaching language
Spring. Offered spring 2021.
Assistant Professor Alexander Willén, Department of Economics.