STEM / applied
Econometrics · Undergraduate · Math
Learning objectives from the Econometrics syllabus, grouped by unit. Click an objective for study materials.
Topics typically covered
Click a topic for the full text and related unit practice.
Undergraduate Econometrics — outline derived from course README sections and typical US statistics syllabi (OpenIntro / standard OER where applicable).
Linear econometric models
- Classical linear regression assumptions (Gauss–Markov) — Classical linear regression assumptions (Gauss–Markov)
- Heteroskedasticity: detection — Heteroskedasticity: detection
- Robust SEs — Robust SEs
- Autocorrelation in time series regressions — Autocorrelation in time series regressions
- Instrumental variables (introduction) — Instrumental variables (introduction)
- Simultaneous equations models (overview) — Simultaneous equations models (overview)
Panel and time series data
- Pooled and fixed effects — Pooled and fixed effects
- Random effects models (intro) — Random effects models (intro)
- Difference-in-differences (introduction) — Difference-in-differences (introduction)
- Stationarity — Stationarity
- Spurious regression — Spurious regression
- ARIMA in econometric forecasting (intro) — ARIMA in econometric forecasting (intro)
- Cointegration overview (optional) — Cointegration overview (optional)
Policy and interpretation
- Dummy variables for policy interventions — Dummy variables for policy interventions
- Reading empirical economics papers — Reading empirical economics papers
- Specification tests — Specification tests
- Model diagnostics — Model diagnostics
- Limitations of observational econometrics — Limitations of observational econometrics
Learning objectives
Click an objective for study materials.
Linear econometric models
- Classical linear regression assumptions (Gauss–Markov) — Classical linear regression assumptions (Gauss–Markov)
- Heteroskedasticity: detection — Heteroskedasticity: detection
- Robust SEs — Robust SEs
- Autocorrelation in time series regressions — Autocorrelation in time series regressions
- Instrumental variables (introduction) — Instrumental variables (introduction)
- Simultaneous equations models (overview) — Simultaneous equations models (overview)
Panel and time series data
- Pooled and fixed effects — Pooled and fixed effects
- Random effects models (intro) — Random effects models (intro)
- Difference-in-differences (introduction) — Difference-in-differences (introduction)
- Stationarity — Stationarity
- Spurious regression — Spurious regression
- ARIMA in econometric forecasting (intro) — ARIMA in econometric forecasting (intro)
- Cointegration overview (optional) — Cointegration overview (optional)
Policy and interpretation
- Dummy variables for policy interventions — Dummy variables for policy interventions
- Reading empirical economics papers — Reading empirical economics papers
- Specification tests — Specification tests
- Model diagnostics — Model diagnostics
- Limitations of observational econometrics — Limitations of observational econometrics
Multi-Unit Problems
Course-level sets that combine skills across study units (coming soon).
Browse Multi-Unit ProblemsWhat each unit includes
Open a unit below for full materials. Typical resources:
- Study guide
- Exam Strategy
- Common Mistakes
- Worksheets
- Word problems
- Mixed Practice
- Multi-Unit Problems
- Review
- Practice test
- Answer key
Study units
Each unit includes a study guide, worksheets, review, practice test, and answer key. One unit is free; subscribe for the full class.
- Linear econometric models
Classical linear regression assumptions (Gauss–Markov)
Coming soon - Panel and time series data
Pooled and fixed effects
Coming soon - Policy and interpretation
Dummy variables for policy interventions
Coming soon
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$1,162 · Econometrics · 18 tutoring hrs
Study guides, worksheets, reviews, practice tests, and answer keys for 1 class. 18 tutoring hours (1 hr / week · semester). Bundle discount applied vs buying separately. Pay in full via Zelle.