3 credits
Spring 2026 Lecture Upper Division
This is an advanced applied econometrics course that introduces students to a variety of techniques and methods for the empirical study of economic data. This course focuses on the practical aspects of economic data analysis, including data organization (such as merging and appending datasets) and the implementation of econometric models. This course will cover multivariate linear regression, non-linear least squares, models for binary and categorical outcomes, model selection using machine learning, and panel data models. As part of the course, students will learn to conduct empirical analysis of economic data using Stata, a statistical software package.
Learning Outcomes1Explore and organize economic data.
2Apply a variety of econometric techniques including: multivariate linear regression, non-linear least squares, models for binary outcomes (including logit and probit models), and models for panel data (including linear random effects and fixed effects models).
3Apply machine learning fundamentals and techniques to perform effective model selection and evaluation.
4Interpret and critique the results from econometric estimations.
Prerequisites
made by Purdue students.