Course Details

Course Number: 94-835

Applied Econometrics II

Units: 6

Econometrics is the statistical analysis of causal relationships in human affairs. Econometrics is essential for advancing understanding in the social sciences, conducting public policy evaluation, and assessing the impact of business practices.

Applied Econometrics I and II is an integrated two-course sequence designed to teach the essentials of the econometric methodology. Econometrics I covers random assignment, multiple regression, and instrumental variables methods. Econometrics II covers regression discontinuity, difference-in-differences techniques, event study analysis, and synthetic control methods.

Both Applied Econometrics I and Applied Econometrics II are “hands on” courses. Students learn to read and interpret existing studies, but also to conduct econometric analyses of their own.

Pre-requisites:

Applied Econometrics II: Students are presumed to have taken 94-834 (Applied Econometrics I), and to have a solid grounding in basic statistics, at the level of 90-711 (Empirical Methods for Public Policy and Management), 90-786 (Intermediate Empirical Methods) or 95-796 (Statistics for IT Managers). We will make good use of the material covered in those courses.


Learning Objectives:

1. Identify the main threats to validity in econometric research studies.
2. Evaluate the assumptions of common econometric models and estimation methods.
3. Explain the underlying econometric theory behind regression discontinuity, difference in differences and instrumental variables methods.
4. Develop competence accessing common data sources used in econometric studies.
5. Apply a range of econometric methods for causal inference with observational data.
6. Formulate a research design, indicating the research question, data sources, methods and estimation strategy.
7. Critique policy analysis studies that use common econometric models and estimation methods.

Syllabus

Prerequisites:
94-834 Applied Econometrics I 6 Credits
95-796 Statistics for IT Managers 6 Credits
90-711 Statistical Reasoning with R 12 Credits
90-777 Intermediate Statistical Methods 6 Credits

Faculty:
Akshaya Jha
Edson R Severnini