Econometrics
Undergraduate · Statistics
Syllabus focus
Standard syllabus · STEM / applied
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Topics typically covered
Standard syllabus
Linear econometric models
- Classical linear regression assumptions (Gauss–Markov)
- Heteroskedasticity: detection and robust SEs
- Autocorrelation in time series regressions
- Instrumental variables (introduction)
- Simultaneous equations models (overview)
Panel and time series data
- Pooled, fixed, and random effects models (intro)
- Difference-in-differences (introduction)
- Stationarity and spurious regression
- ARIMA in econometric forecasting (intro)
- Cointegration overview (optional)
Policy and interpretation
- Dummy variables for policy interventions
- Reading empirical economics papers
- Specification tests and model diagnostics
- Limitations of observational econometrics
STEM / applied
Applied econometrics
- Replication exercises with published datasets
- Stata or R (plm, fixest) for panel models
- FRED and census data for macro/micro applications
- Visualization of regression results for policy briefs
- Robustness checks and sensitivity analysis
- Writing an empirical economics research memo
Additional applied practice
- Reviewing assumptions with domain experts
- Documenting analysis choices for reproducibility
- Sensitivity analyses for key modeling decisions
- Connecting results to the original research or business question
Notes
Parallel to econometrics courses in economics departments. Emphasizes time series, panel data, and identification at an undergraduate level.