Course Roadmap
A structured introduction to econometrics that moves from foundations and regression to causal inference, applied methods, and research design.
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About This Course
This course is designed for students who want a clear path into econometrics. It begins with the core language of economic data and regression, then develops statistical inference, panel data methods, experimental and quasi-experimental designs, and practical research skills.
Each module can later be turned into its own page with lessons, examples, formulas, and quizzes. For Google Sites, this page works well as a central roadmap page.
17
Modules
Lessons
Concepts, examples, and review
4
Course Parts
Exercises
Quizzes and applied interpretation
Part I — Foundations
Build the language and logic of econometrics before moving into advanced methods.
Module 1 — Foundations of Econometrics
What econometrics is, why it matters, variables, samples and populations, and correlation versus causation.
Module 2 — Regression Basics
Regression intuition, simple and multiple regression, functional forms, and goodness of fit.
Module 3 — Statistical Inference
Standard errors, p-values, confidence intervals, robust inference, and clustering.
Module 4 — Regression Problems and Bias
Omitted variable bias, selection bias, reverse causality, measurement error, and endogeneity.
Part II — Core Models
Learn the most common model types and data structures used in applied econometrics.
Module 5 — Limited Dependent Variable Models
Linear probability model, logit, probit, and marginal effects for binary outcomes.
Module 6 — Data Types
Cross-sectional data, time series, panel data, repeated cross-sections, and research design choices.
Module 7 — Panel Data Methods
Fixed effects, random effects, Hausman testing, first differences, and serial correlation.
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