Big Data Econometrics

Catalog description

The goal of this course is to introduce students to a wide range of methods, which are designed to tackle commonly seen real-world problems, and are intensively used in the current literature. These methods include linear regression, logistic regression, bootstrapping, cross validation, bagging, boosting, splines, random forests, neural networks, and support vector machines. This course is application oriented. We will emphasize the intuition behind each method and touch on a little bit of theory.