Econometrics with R

MediumComputer ScienceEconomics16 chapters

Bridge the gap between mathematical theory and practical code by applying rigorous econometric models to real-world data in R. You will learn to isolate causal effects and forecast complex trends through a transparent, reproducible analytical workflow.

What This Course Covers

Econometrics with R is structured into 16 chapters that build on each other progressively:

Chapter 1: Foundations: R & RStudio for Data Science▼

Chapter 2: The Math of Uncertainty: Probability Theory▼

Chapter 3: A Review of Statistics▼

Chapter 4: Simple Regression: Modelling Linear Trends▼

Chapter 5: Inference: Testing Hypotheses in Simple Models▼

Chapter 6: Multiple Regression: The Power of Ceteris Paribus▼

Chapter 7: Testing Complex Hypotheses▼

Chapter 8: Nonlinearities: Polynomials and Logarithms▼

Chapter 9: Logit and Probit Models▼

Chapter 10: Evaluating Internal & External Validity▼

Chapter 11: Controlling for Unobserved Constants▼

Chapter 12: Instrumental Variables: Solving Endogeneity▼

Chapter 13: Randomized Trials & Quasi-Experiments▼

Chapter 14: Introduction to Time Series▼

Chapter 15: Estimating Causal Effects over Time▼

Chapter 16: VARs, Cointegration, and ARCH▼

Each chapter combines interactive AI tutoring with hands-on examples. After you learn the material, Lambdio's spaced repetition algorithm schedules review sessions at optimal intervals — so you retain concepts and techniques long-term.

How to Study Econometrics with R on Lambdio

Lambdio's AI-powered platform adapts to how Computer Science courses are best learned. Here's our recommended approach:

Learning Mode

Standard Mode — for first-time learning of each chapter

Review Modes

Standard, Quiz — for spaced repetition reviews

Learning Priority

High Priority — controls how often the algorithm schedules reviews

Econometrics with R is a code-heavy, mathematically rigorous subject that blends economic theory, statistical inference, and practical R programming. Standard Mode is the right choice because learning econometric models — from OLS to instrumental variables and time series — requires structured explanations, mathematical derivations, code demonstrations, and comprehension checks at every step. Socratic Mode is less suitable since discovering Gauss-Markov assumptions or two-stage least squares through questions alone would be inefficient without direct mathematical and computational exposition. The Medium difficulty of this course, combined with its heavy reliance on both mathematical reasoning and R coding, makes High priority the ideal default — the spaced repetition algorithm will schedule frequent reviews so that core concepts (endogeneity, identification, stationarity) and R implementation patterns remain locked in long-term memory. For quick reinforcement of key definitions and diagnostics, pair Standard Mode with Quiz Mode during review sessions.

Interactive Quiz

Test your knowledge with these sample questions from the course. Tap an answer to see if you're right:

Q1: What does OLS minimize in simple linear regression?

Q2: Under heteroskedasticity, which standard errors should be used for valid inference?

Q3: Omitted variable bias occurs when:

Q4: What does the F-statistic test in a multiple regression model?

Q5: In a log-log regression model, the coefficient on X represents:

Q6: What are the two conditions for a valid instrumental variable?

Q7: The Differences-in-Differences estimator controls for:

What You'll Be Able to Do After This Course

✓Write and execute R code for econometric analysis using core data structures, functions, and packages
✓Apply probability theory and asymptotic concepts (LLN, CLT) to quantify uncertainty in economic relationships
✓Specify, estimate, and interpret simple and multiple linear regression models using OLS
✓Diagnose and address model violations including heteroskedasticity, multicollinearity, and omitted variable bias
✓Implement nonlinear, binary-choice (Logit/Probit), and panel data models for real economic data
✓Use instrumental variables and quasi-experimental methods (DID, RDD) to establish causal relationships
✓Build time series models including AR, ADL, VAR, and cointegration for forecasting economic trends
✓Evaluate internal and external validity of empirical research designs and identify common threats