ECS503 Advanced Econometric
Spring 2019Autumn 2019
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 inference and identification with method of moments, maximum likelihood, panel data models, identification and natural experiments, applied instrumental variables, differences in differences and regression discontinuities. We also cover some further econometric topics such as choice models, measurement error and missing data, dependent data, limited dependent variables, and computer literacy.
Upon completion of the course, the students will be able to:
- Identify and understand the standard identification strategies in research papers.
- Calculate standard estimators and corresponding standard errors
- Discuss inferential issues with dependent data.
- Transform raw data into canonical useful forms.
- Use available software to estimate standard models on real world data.
- Evaluate alternative identification strategies.
Lectures and assignments.
Requirements for course approval
Approved assignments, termpaper, and participation in class
Written 4-hour school exam.
Grading scale: Pass/Fail
Stata and subversion.
In addition to selected papers, there are two textbooks:
Linton, Oliver (2017). Probability, Statistics and Econometrics, Academic Press, 1st edition
Angrist and Pischke (2009). Mostly Harmless Econometrics: An Empiricist's Companion, Princeton, NJ: Princeton University.
- ECTS Credits
- Teaching language
Autumn. Offered autumn 2018
Assistant professor Morten Sæthre, Department of Economics.
Lecturers: Morten Sæthre and professor Katrine Vellesen Løken