Teaching
ECON 418-518: Introduction to Econometrics (Fall 2024)

ECON 418-518: Introduction to Econometrics (Fall 2024)

Course Evaluations

For the full course evaluations, click here: Course Evaluations. Here are a few selected student evaluations:

  1. I started this course in tears, but it ended up becoming one of my favorite classes! It wouldn’t have been possible without the professor’s support.
  2. I have never had an instructor grade as quickly or have as much availability to assist students outside of class. Will replied via Slack incredibly fast and always addressed concerns/questions in-class, via D2L announcements, and slack. All the materials were super well organized, and the syllabus and class schedule was actually true to the class. Deadlines and expectations were very clear.
  3. Great instructor that genuinely knows and cares about the material and his students.
  4. Brasic has a lot of promise to become a very strong lecturer and already demonstrates his potential as an academic and professional.
  5. Will is a competent and professional instructor. The course was challenging, but rewarding.
  6. Will was a great professor. Appreciated his lectures.
  7. I liked how there were so many practical aspects to this course. It is definitely something I brag about to recruiters.
  8. I liked the difficulty of the class. This was the hardest class I have taken, but the most interesting and relevant to the industry in which I want to pursue a career. The professor was also very understanding and flexible and helped us learn the content in the best way possible.
  9. I thought that it was a very challenging course that is very applicable to the real world and that was appreciated.
  10. The professor had a very helpful nature and kind attitude that helped foster a fantastic learning environment.
  11. The professor wasn’t afraid to dive deeper into the math which was very beneficial.
  12. Will Brasic was a good teacher. He was willing to adapt and change parts of the course as it was happening, such as changing the format of the final exam.
  13. I really enjoyed how much practice and engagement with the material I was given in the course. I was acclimatized to course content and a lot of the learning.
  14. He was a very nice grader and truly tried to get you to understand the content.
  15. Really enjoyed the class & feel that it gave me some valuable skills.

Welcome!

This is the course website for ECON 418-518: Introduction to Econometrics at The University of Arizona in-person during the Fall 2024 semester. Here, you will be able to access the lecture slides, homeworks, and exams for the entire course along with R code used to teach the language. For the syllabus, click here: Syllabus. For the welcome letter, click here: Welcome Letter.

This course will provide you with a comprehensive introduction to econometrics, a crucial tool for identifying causal effects. We will start by understanding the basics of econometrics and reviewing the essential mathematical concepts necessary for success in this course. Following this, we will delve into the fundamental building blocks of econometrics, focusing on simple and multiple linear regression using ordinary least squares. We will explore the assumptions underlying the linear model, discuss the consequences of violating these assumptions, and learn techniques to address these issues. Additionally, we will go beyond linear regression to cover more advanced econometric estimators.

In the final third of the course, we will transition to machine learning, a rapidly evolving field that is increasingly influencing econometrics and economics as a whole. We will begin by understanding the core principles and comparing them to traditional econometrics. The remainder of the course will be dedicated to learning some of the most prominent machine learning algorithms.

Moreover, you will gain proficiency in R, a powerful language and environment for statistical computing and graphics. R is widely used by economists and statisticians, and developing expertise in it will be a valuable asset for your resume and career prospects. I strongly encourage you to invest time in mastering R.

Lecture Slides

Below are the lecture slides associated with Introductory Econometrics: A Modern Approach by Jeffrey M. Wooldridge.

  1. Introduction to Econometrics
  2. Math Review
  3. Simple Linear Regression (SLR)
  4. Matrix Algebra
  5. Multiple Linear Regression (MLR)
  6. Inference
  7. Asymptotics
  8. MLR Model Selection
  9. Qualitative Information
  10. Heteroskedasticity
  11. MLR Issues
  12. Panel Data Estimators
  13. Binary Response Models

Below are the lecture slides associated with Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani.

  1. Introduction to Machine Learning
  2. Resampling Methods
  3. Shrinkage Estimators
  4. Random Forests

Homeworks

Below are the homeworks associated with the course.

  1. Homework 1 Instructions
  2. Homework 2 Instructions
  3. Homework 3 Instructions

Exams

Below are the exams associated with the course.

  1. Exam 1
  2. Exam 2
  3. Exam 3

R Code

Below are the R codes associated with the course.

  1. R Tutorial
  2. Math Review
  3. Simple Linear Regression (SLR)
  4. Matrix Algebra
  5. Multiple Linear Regression (MLR)
  6. Inference
  7. Asymptotics
  8. MLR Model Selection
  9. Qualitative Information
  10. Heteroskedasticity
  11. MLR Issues
  12. Panel Data Estimators
  13. Binary Response Models
  14. Resampling Methods
  15. Shrinkage Estimators
  16. Random Forests