EasyEcon
Economics made easy - interactive notebooks to rapidly speed up your learning.
EasyEcon spans macroeconomics, microeconomics, and econometrics. Most models are authored in Python with Marimo, exported to the browser, and presented inside an editorial Astro shell.
Interactive models published as static web apps.
Core tracks now span theory, policy, and empirical identification.
Advanced additions now extend the library beyond the original growth and price-theory core.
Model library
Browse the growing library by category.
Growth, business cycles, and open economy
Macroeconomics
From Solow and Ramsey to business-cycle dynamics, overlapping generations, endogenous growth, and open-economy adjustment.
8 published modelsPrice theory to strategic interaction
Microeconomics
From partial-equilibrium price theory into consumer choice, firm behavior, and game theory.
7 published modelsRegression, identification, and simulated evidence
Econometrics
Marimo-first econometrics notebooks on OLS, causal identification, panel intuition, and simulated data-generating processes.
4 published modelsWhat’s new
The first econometrics wave is now live inside the same Marimo-first stack.
The catalog now stretches from macro and micro theory into econometrics notebooks on simple OLS, omitted-variable bias, difference-in-differences, and instrumental variables. The new notebooks use deterministic simulated data so the identification story stays stable and teachable.
A hybrid path still exists for future browser-native interactions, but the default stack remains Python for model logic and static Astro pages for the publishing layer. The site shell still supports a persisted light-or-dark reading mode around those embeds.
Econometrics wave
Start with these four empirical teaching notebooks.
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.
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.
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.