ECS509 PhD Econometrics II

• Topics

  This is the second of the two courses in econometrics in the PhD specialisation 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 is 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 specialised applied topics such as bad controls, measurement error, and clustering. Lastly, we will study applications on topics like market power, education, and sustainability.

• Learning outcome

  Upon completion of the course, the student can:

  Knowledge:

  • Identify and discuss the standard identification strategies in research papers.
  • Master applications on topics such as market power, education, and sustainability.

  Skills:

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

  General competencies:

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

• Teaching

  The course consists of lectures, presentations and assignments.

• Restricted access

  • PhD candidates at NHH
  • PhD candidates at Norwegian institutions
  • PhD candidates at other institutions
  • PhD candidates from the ENGAGE.EU alliance
  • Motivated master’s students at NHH may be admitted after application, but are subject to the approval from the course responsible on a case by case basis

• Recommended prerequisites

  ECS508.

• Compulsory Activity

  Approved referee assignment and class participation. 

  Compulsory activities (work requirements) are valid for one semester after the semester they were obtained.

• Assessment

  1. Group-based replication project of maximum 4000 words. Group size is restricted to 2-3 students.

  The assignment will be distributed in January. The submission deadline will be in April. 

  2.  Group-based presentation of an assigned paper. Group size is restricted to 2 students.

  The presentations will happen continuously throughout the course. Division of groups and dates for presentations will be done in January.

  Students must pass both elements in order to successfully complete the course.

  Re-take is offered the semester after the course was offered for students with valid compulsory activities (work requirements).

• Grading Scale

  Pass/Fail.

• Literature

  In addition to selected papers provided in Canvas, there is one textbook:

  Cunningham, Scott (2021). Causal Inference: The Mixtape (Yale University Press, USA)

• This is an ENGAGE-course

  This course is offered to PhD candidates from the ENGAGE.EU alliance.