# M504: Econometrics I

This course introduces students to basic econometric concepts and their application. Compared to most undergraduate econometrics classes, we will put more emphasis on the mathematical and statistical foundations of econometric methods. Therefore, students are expected to have a strong background in Linear Algebra and Calculus. We will start with a review of matrix algebra, probability theory and statistics. Afterwards, the course covers linear regression models, which are the foundation of many econometric and machine learning methods, in detail. We will discuss (and prove) finite-sample and asymptotic properties of the OLS estimator. We continue with a discussion of statistical testing and conclude with an outlook on generalizations of the OLS estimator that tackle many prevalent problems. In several tutorial sessions, students will learn how to apply econometric methods to data using the statistical software R.

This class is cross-listed as E471: Econometric Theory and Practice I, which is an advanced Econometrics course for Economics majors.

All lecture material is available on Canvas.