Welcome to Econometrics with Simulations#
Econometrics, as a discipline that combines economic theory, statistics, and mathematics, can be challenging. Abstract concepts and mathematical proofs, although fundamental, sometimes hinder the intuitive understanding of econometric principles. This book is born from the conviction that a good way to understand econometrics is by experimenting with it.
Through interactive simulations, this book allows you to visualize and experiment with econometric concepts in real-time. The proposal consists of modifying parameters and assumptions, observing how results change to develop a deeper and practical understanding of econometric methods. Each chapter includes simulations that allow you to “play” with the concepts, making learning more dynamic and memorable. The book is designed as a resource for both students and practicing professionals. We aim for it to be useful as a complement for students, but also as a reference tool for those designing analysis strategies and experiments, allowing them to make informed decisions about different methodological aspects, such as sample size or the inclusion of variables.
đź“ť Note on this preliminary edition#
This document represents a preliminary version of “Econometrics with Simulations”. The chapters and linked dashboards (marked with hyperlinks) are available as a preview and demonstrate the interactive approach we seek in this project. The content of this project is under active development, with regular updates. We invite readers to explore the available simulations and follow the project’s progress.
Last update: March 2026 Status: Preliminary version 0.1
This page presents two resources to guide reading: the index of available contents and the index of currently published interactive simulations. Each section links directly to the corresponding chapters and dashboards. For the full planned index of the book, including chapters and simulations under development, see the Planned Book Index.
Available Contents#
Introduction#
Simple Regression#
Multiple Regression#
Available Interactive Simulations#
Each simulation is embedded in the corresponding chapter of the book. The links below lead directly to the section where each dashboard can be found.
Simple Regression#
Statistical Properties: Unbiasedness and Variance — Illustrates the unbiasedness of OLS estimators and the determinants of their variance across repeated samples.
Inference: T-Test in Regression — Explores the sensitivity of the T-test to the normality assumption on errors.
Power and Optimal Sample Size — Allows computing statistical power and the minimum sample size needed to detect a given effect.
Multiple Regression#
Multicollinearity — Shows the impact of multicollinearity on estimates, standard errors, and model interpretation.
Omitted Variable Bias — Shows the biases generated by omitting a relevant variable.
Causal Inference#
Identification under C.I.A.: Regression versus Random Forests — Compares causal effect estimation methods valid under the Conditional Independence Assumption.
Authors#
Contributors#
Supporting Institutions#
