Econometrics
Undergraduate · Economics
Syllabus focus
Topics typically covered
Standard syllabus
Regression foundations
- Probability and statistics review for econometrics
- Simple and multiple linear regression
- Gauss–Markov assumptions and OLS properties
- Hypothesis tests and confidence intervals
- Functional form, dummy variables, and interactions
- Goodness-of-fit and prediction
Violations and extensions
- Heteroskedasticity and robust SEs
- Autocorrelation (intro)
- Multicollinearity diagnostics
- Omitted variables and measurement error
- Limited dependent variables survey (logit/probit intro)
- Time-series basics or panel intro (course-dependent)
Causal inference intro
- Potential outcomes language
- Randomized experiments as gold standard
- Matching and selection-on-observables (survey)
- Instrumental variables intuition
- Difference-in-differences intro
- Regression discontinuity awareness
STEM / applied
Software practice
- Estimate regressions in Stata/R/Python (course stack)
- Replicable do-files/scripts and logging
- Table exports for papers
- Diagnostic plots and residual analysis
- Working with real cross-section/panel datasets
- Version control for empirical projects (intro)
Empirical workflow
- Asking a feasible empirical question
- Data cleaning and codebook discipline
- Robustness checks and specification sensitivity
- Reading applied papers’ tables critically
- Research ethics and data provenance
- Capstone: short empirical paper with replication archive
Notes
Overlaps math [`econ_metrics`](content/courses/math/undergraduate/econ_metrics/README.md) when both exist; tutoring aligns to the economics department’s software and identification emphasis.