ECS508 PhD econometrics I

• Topics

  This is the first of two courses in econometrics in the PhD program in economics. The goal of the courses 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.

• Learning outcome

  Upon completion of the course, the students will be able to:

  Knowledge:

  • Identify and understand the standard identification strategies in research papers

  Skills:

  • Calculate standard estimators and corresponding standard errors
  • Discuss inferential issues with dependent data

  General competencies:

  • Use available software to estimate standard models on real world data
  • Evaluate alternative identification strategies

• Teaching

   Lectures and assignments.

• Required prerequisites

  Master-level econometrics.

• Requirements for course approval

  Approved assignments and participation in class.

• Assessment

  Written 4-hour school exam.

• Grading Scale

  Pass/Fail.

• Literature

  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.