Econometrics track

Simulated data, known truth, and identification logic you can see move.

This track begins with simple OLS, then moves through omitted-variable bias, difference-in-differences, regression discontinuity, and instrumental variables. Deterministic simulated data keeps the ground truth visible while each model isolates a different identification problem or solution.

Foundations

Start with regression fit, residuals, and coefficient interpretation.

Econometrics Regression Intermediate EasyEcon / Marimo

Upper-undergraduate econometrics

Simple OLS Regression

Adjust the true intercept, slope, noise, and sample size to relate realised estimates to fit, slope estimation error, and an approximate 95% slope interval.

Fitted line, residuals, precision, and known truth Open model

Identification methods

Then move into confounding and quasi-experimental identification.

Econometrics Causal inference Advanced EasyEcon / Marimo

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.

Bias direction, confounding strength, and benchmark truth Open model
Econometrics Panel data Advanced EasyEcon / Marimo

Upper-undergraduate econometrics

Difference-in-Differences

Adjust treatment effects, group gaps, noise, and trend violations to see exactly when the DiD estimate matches the truth and when it drifts.

Parallel trends, group means, and treatment-effect decomposition Open model
Econometrics Causal inference Advanced EasyEcon / Marimo

Upper-undergraduate econometrics

Instrumental Variables

See when IV improves on OLS, when weak instruments make the estimate unstable, and how exclusion violations undermine the design.

Endogeneity, relevance, exclusion, and weak instruments Open model
Econometrics Causal inference Intermediate Native JS

Intermediate causal inference

Regression Discontinuity and the Cutoff Jump

See how a discontinuity identifies a causal effect: fit a line each side of the cutoff and read the vertical jump between them, then watch the bandwidth trade bias against noise.

The cutoff, the local-linear fits, and the jump that estimates the treatment effect Open model
Econometrics Time series Intermediate Native JS

Intermediate econometrics

The AR(1) Process: Persistence, Mean Reversion, and the Unit Root

Drag the persistence parameter through one and watch a stationary, mean-reverting series become a random walk and then explosive — while the sample ACF tracks the theoretical geometric decay φᵏ.

The simulated path, the ACF, and the phase change at φ = 1 Open model

Also browse

Move across the broader economics catalogue.

All models

Growth, business cycles, and open economy

Macroeconomics

From Solow and Ramsey to business-cycle dynamics, overlapping generations, endogenous growth, and open-economy adjustment.

Price theory to strategic interaction

Microeconomics

From partial-equilibrium price theory into consumer choice, firm behaviour, and game theory.