221.651.01
Econometrics I
Location
East Baltimore
Term
3rd Term
Department
International Health
Credit(s)
4
Academic Year
2025 - 2026
Instruction Method
In-person
Tu, Th, 8:30 - 10:20am
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
No
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
Every Year
Resources
Prerequisite
140.622 or 140.652
Enrollment Restriction
This course is not restricted.
This course equips students with the tools to evaluate relevant policy questions in public health using economic models and real-world data. Students will learn to combine econometric methods with AI-assisted tools such as ChatGPT for data cleaning, reproducible coding, vibe coding, and robustness checks. By the end, students will have the skills to conduct rigorous applied research using survey and claims datasets, preparing them for advanced coursework and for policy- and practice-relevant analysis in domestic and international contexts.
Introduces the foundations of econometrics with emphasis on applications to economics and public health. Includes topics such as linear regression, fixed effects with pooled and panel data, instrumental variables, difference-in-differences, interrupted time series, event analysis, and twin models. Uses household survey and administrative claims data during hands-on exercises. Integrates Stata with ChatGPT, introducing students to Agentic Econometrics—leveraging generative AI to clean and structure data, code reproducibly, and automate robustness checks. Prepares students to apply econometric methods for analyzing economic models and its application in evaluating public health interventions.
Learning Objectives
Upon successfully completing this course, students will be able to:
- Apply and analyze statistics and econometric methods (including statistical inference, regression models, and applied econometric designs) to solve public health problems
- Implement core econometric techniques such as linear regression, fixed effects for pooled and panel data, instrumental variables, Interrupted Time Serie, Event Analysis, and other causal inference strategies
- Use Stata and ChatGPT collaboratively to design, code, and execute empirical projects
- Prepare and manage large datasets, conduct statistical inference, and critically interpret empirical results
- Evaluate the strengths and limitations of empirical evidence to inform policy and decision-making in both domestic and international settings
Upon successfully completing this course, students will be able to:
Methods of Assessment
This course is evaluated as follows:
- 60% Assignments
- 40% Final Exam
Each week, the first section we will cover the conceptual foundation and in the second session students will apply the concepts using a specific dataset. Students will be asked to bring their laptop to the class.
Personal laptops preloaded with Stata are required for use during class.