ECS509 PhD Econometrics II

• Topics

  This is the second of the two courses in econometrics in the PhD specialization 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. Lastly, we will study applications on topics like market power, education, and sustainability.

• Learning outcome

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

  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:

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

• Teaching

  Lectures and assignments.

• Restricted access

  • PhD candidates from NHH
  • PhD candidates from University of Bergen
  • PhD candidates from other higher educational institutions
  • Promising master students if approved by course responsible

• Recommended prerequisites

  ECS508.

• Required 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.

  Group size: 2.

  Scope: 4000 words maximum.

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

  2. Group-based presentation of an assigned paper

  Group size: 2

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

  Both assignments are equally important, and students must pass both of them in order to successfully complete the course.

  As the assessment in this course by its nature cannot be re-examined, the grades awarded may not be appealed.

  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 on canvas, there is one textbook:

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