Course #
PPHA 42100
Winter 2022
Certificate Program
Data Analytics

This course is the second in a three-part sequence designed to cover applied econometrics and regression methods at a fairly advanced level. This course provides a theoretical analysis of linear regression models for applied researchers. It considers analytical issues caused by violations of the Gauss-Markov assumptions, including linearity (functional form), heteroscedasticity, and panel data. Alternative estimators are examined to deal with each. Prerequisites: This course is intended for first or second-year Ph.D. students or advanced masters-level students who have taken Applied Econometrics I. Familiarity with matrix algebra is necessary.


Harris PhD & MA CRM students or instructor permission required.