ECON 7772 Econometric Methods
This course provides an understanding of the econometric theory that underlies common econometric models. The focus is on regression models and their many extensions. Topics include finite and asymptotic properties of estimators, consistency and limiting distributions, specification issues, heteroskedasticity, autocorrelation, endogeneity and simultaneity, and nonlinear model estimators including maximum likelihood and the generalized method of moments.
Course overview
- Department
- Economics
- School
- MCAS
- Credits
- 4
- Level
- Graduate
- Offered
- Every Spring
Catalog details
- Prerequisites
- ECON7770
Course evaluations
- Course rating
- 4.44 / 5 · Very Good
- Instructor rating
- 4.47 / 5 · Very Good
- Terms evaluated
- 5
| Term | Instructor | Course overall | Instructor overall | Well organized | Clear learning objectives | Intellectually challenging | Helpful assignments | Applicable beyond course | Attendance necessary | Weekly effort | Responses |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Spring 2026 | Arthur Lewbel | 4.5 | 4.6 | 4.7 | 4.7 | 4.8 | 4.6 | 4.8 | 4.6 | 4–6 hrs/week | 14 of 14 |
| Spring 2026 | Shengtao Dai | 4.5 | 4.1 | 4.6 | 4.8 | 4.8 | 4.6 | 4.6 | 4.6 | 4–6 hrs/week | 14 of 14 |
| Spring 2025 | Arthur Lewbel | 4.6 | 4.7 | 4.8 | 4.8 | 4.9 | 4.8 | 4.7 | 4.7 | 4–6 hrs/week | 19 of 19 |
| Spring 2025 | Shengtao Dai | 4.7 | 4.6 | 4.8 | 4.8 | 4.9 | 4.8 | 4.8 | 4.8 | — | 19 of 19 |
| Spring 2024 | Arthur Lewbel | 4.2 | 4.4 | 4.7 | 4.6 | 4.5 | 4.4 | 4.6 | 4.4 | 10+ hrs/week | 17 of 17 |
| Spring 2024 | Shengtao Dai | 4.2 | 4.1 | 4.6 | 4.6 | 4.6 | 4.5 | 4.4 | 4.5 | 10+ hrs/week | 17 of 17 |
| Spring 2023 | Arthur Lewbel | 4.5 | 4.9 | 4.9 | 4.8 | 4.8 | 4.8 | 4.8 | 4.7 | 4–6 hrs/week | 19 of 20 |
| Spring 2023 | Qingsong Yao | 4.9 | 4.6 | 4.8 | 4.8 | 4.6 | 4.7 | 4.8 | 4.4 | — | 19 of 20 |
| Spring 2022 | Qingsong Yao | 4.0 | 4.5 | 3.8 | 4.0 | 4.3 | 4.0 | 4.0 | 4.0 | — | 7 of 22 |
| Spring 2022 | Shakeeb Khan | 3.5 | 3.5 | 3.4 | 4.0 | 4.5 | 3.9 | 3.9 | 3.6 | — | 8 of 22 |
Data freshness
Instructors
Sections
- Section 01
- Section 02