What to explore
Change parameters and watch the model adjust.
- True treatment effect, instrument strength, endogeneity, exclusion violation, and sample size
- The notebook shows the first stage and the outcome relation side by side
Upper-undergraduate econometrics
Instrumental Variables
A simulated endogeneity notebook comparing OLS and IV under changes in instrument strength, endogeneity, and exclusion validity.
Focus
Endogeneity, relevance, exclusion, and weak instruments
See when IV improves on OLS, when weak instruments make the estimate unstable, and how exclusion violations undermine the design.Core ideas
Interpret the mechanics before you chase the graphs.
- An instrument must move the endogenous regressor and stay out of the outcome except through that channel.
- IV can correct OLS bias when the instrument is valid, but weak first stages reduce precision.
- Exclusion violations can make IV look authoritative while still being wrong.
Learning goals
What this model should help students internalize.
- Compare OLS and IV when the regressor is endogenous.
- Interpret the relevance and exclusion conditions as distinct design requirements.
- See why weak instruments make IV noisy even when the design is conceptually correct.
Prerequisites
Concepts to review before diving in.
- Omitted-variable-bias intuition
- Comfort with the idea of an external source of variation
Next models to study
Keep moving through the track.
Upper-undergraduate econometrics
Simple OLS Regression
Adjust the data-generating process and watch the fitted line, coefficient estimates, and residual behavior move with noise, slope, and sample size.
Upper-undergraduate econometrics
Omitted Variable Bias
Change confounding strength and the omitted variable's effect to compare the true coefficient, the naive regression, and the fully controlled benchmark.