Course type
IWH-DPE Foundation Course, CGDE First-year Course
Lecturer
Professor Dr Felix Noth (IWH, OvGU)
Summary
This is the first course of the first-year triple sequence in Econometrics.
Econometrics I introduces state-of-the-art methods in causal inference for microeconometric applications. We revisit foundational techniques such as linear regression, panel-data methods, and instrumental-variables estimation, placing special emphasis on interpreting OLS coefficients and understanding uncertainty via standard errors.
Schedule
19.10.2026 10:30-12:00 and 13:00-14:30 IWH, Leipziger Str. 100, 3rd floor, Conference Room
02.11.2026 10:30-12:00 and 13:00-14:30 IWH, Leipziger Str. 100, 3rd floor, Conference Room
16.11.2026 10:30-12:00 and 13:00-14:30 IWH, Leipziger Str. 100, 3rd floor, Conference Room
30.11.2026 10:30-12:00 and 13:00-14:30 IWH, Leipziger Str. 100, 3rd floor, Conference Room
Content
- Regression Review
- Causality & Potential Outcomes
- Panel Data
- Standard Errors
- Instrumental Variables
Course requirements
Attendance at all lectures is mandatory and a prerequisite to take the final exam.
Students are required to complete four online quizzes, each consisting of true/false and multiple-choice questions. These quizzes assess the content of the corresponding week’s lectures and required readings. To qualify for the final exam, students must pass at least three out of the four quizzes.
The course concludes with a 60-minute, closed-book written final exam. This exam is comprehensive and covers all material from Econometrics I. Only students who have fulfilled the quiz requirement are permitted to sit for the final exam.
Required Reading
Cunningham, S. (2026). Causal Inference: The Remix. Yale University Press.
Regression Review
Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. Chapter 2.
Huntington-Klein, N. (2021). The Effect: An Introduction to Research Design and Causality. Chapman & Hall/CRC. Chapter 13.
Causality & Potential Outcomes
Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. Chapters 3-4.
Huntington-Klein, N. (2021). The Effect: An Introduction to Research Design and Causality. Chapman & Hall/CRC. Chapters 6-9, 10.
Panel Data
Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. Chapter 8.
Huntington-Klein, N. (2021). The Effect: An Introduction to Research Design and Causality. Chapman & Hall/CRC. Chapter 16.
Standard Errors
Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. Chapters 2.25-2.27.
Petersen, M.A. (2008). Estimating standard errors in finance panel data sets: Comparing approaches. The Review of Financial Studies, 22(1), 435–480.
Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. M. (2023). When should you adjust standard errors for clustering? The Quarterly Journal of Economics, 138(1), 1–35.
Wooldridge, J. M. (2023). What is a standard error? (And how should we compute it?). Journal of Econometrics, 237(2), 105517.
Instrumental Variables
Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. Chapter 7.
Huntington-Klein, N. (2021). The Effect: An Introduction to Research Design and Causality. Chapman & Hall/CRC. Chapter 19.
Peter Hull’s lecture notes (https://about.peterhull.net/metrix).
Registration
Please register for the course until September 30, 2026 by sending an e-mail to cgde@iwh-halle.de.