1. Introduction

Welcome to ECON 103: Introduction to Econometrics! Econometrics is the application of statistical methods to economic data to test theories, estimate relationships, and make predictions. It helps economists measure how different factors, like education or income, influence outcomes such as wages or consumption. For example, an econometrician might ask, “Does higher education lead to higher wages?” or “How do interest rates affect investment?”

Introduction to Econometrics was hands-down one of the most valuable courses I took as an undergrad. I mastered essential skills like regression analysis and statistical programming—tools I used daily as a research assistant at the Federal Reserve and continue to rely on today as a researcher at UCLA. But don’t just take my word for it! According to the Bureau of Labor Statistics, data science is one of the fastest-growing careers, with job opportunities expected to soar by 36% from 2023 to 2033.

2. Statistical Programming in R

Econ 103 is taught using R, a powerful and versatile open-source statistical programming language. R ranks among the top programming languages for data analysis, right behind Python and SQL (Stack Overflow Developer Survey, 2024). In this course, you’ll not only learn key econometric methods but also apply them hands-on in R during discussion sessions. To get the most out of this class, I highly recommend familiarizing yourself with R before the first day—it will give you a head start in engaging with the material and mastering the methods we’ll cover.

If you’re just getting started, I’ve gathered some of my favorite resources to help you learn quickly and effectively. For many years, I recommended DataCamp as a top choice for learning R due to its self-paced, interactive format. It also offers certificates and proficiency tests that can be valuable when showcasing your skills to future employers. However, as DataCamp has become more expensive, I now suggest two excellent free alternatives: R for Data Science and YaRrr! The Pirate’s Guide to R. These resources are comprehensive and user-friendly, making them great options for learning R at no cost.

Data Camp Courses

Online Textbooks

Cheat Sheets

3. My Favorite Econometrics Materials

As with any subject, some online resources explain certain concepts better than others. Below, I’ve compiled a collection of my favorite materials for learning econometrics. First, you’ll find the lecture slides from my first Econometrics course at UCSB, taught by Clément de Chaisemartin. This course provided a clear, foundational understanding of regression analysis and was exceptionally well taught. Second, I highly recommend Ben Lambert’s YouTube series on econometrics. His illustrated examples make complex econometric concepts much easier to grasp. In short, it’s the closest thing to a Khan Academy for Econometrics.

UCSB Introduction of Econometrics Lecture Slides

YouTube Videos and Playlists

Disclaimer: Econometrics courses vary across universities, unlike more standardized subjects like calculus or microeconomics. Some professors focus on deriving the OLS estimator and standard errors, while others emphasize statistical programming. Professor Rojas follows the textbook Principles of Econometrics by Hill, Griffiths, and Lim. I found lecture notes from a Rutgers econometrics course that also uses this textbook.

Rutgers Introduction of Econometrics Lecture Slides

  • Lecture 1 - Introduction
  • Lecture 2 - The Simple Linear Regression Model: Specification and Estimation
  • Lecture 3 – Interval Estimation and Hypothesis Testing
  • Lecture 4 – Prediction, Goodness-of-fit, and Modeling Issues
  • Lecture 5 – The Multiple Regression Model
  • Lecture 6 – Further Inference in the Multiple Regression Model
  • Lecture 7 – Using Indicator Variables
  • Lecture 8 – Heteroskedasticity