BMET 25A09 - Advanced Econometrics

This course is a continuation of “Introduction to Econometrics with R”. As such, the course follows the same practical approach to learning Econometrics and R, starting where the introductory section finishes. The course discusses popular methods for causal inference in non-experimental designs, including “Instrumental Variables (IV)”. Students will learn about panel data, that is, data which tracks individuals over time, which allows for modeling unobserved individual heterogeneity. Another important topic of the course is discrete outcomes, e.g., when an individual chooses to buy brand A instead of brand B. Finally, machine learning methods for prediction and classification will be covered

Students need to have taken the course “Introduction to Econometrics with R” at SciencesPo. Alternatively, a course that covers the same syllabus, in the case of international students. Also, students will have to make sure that they are familiar with R knowledge covered in the aforementioned course.

To pass the course, students need to work on individual and collaborative assignments that are listed below: In class quizzes (15%); Midterm take home exam (35%); Final project (50%), where the overall share of the final grade is written between parentheses.