Adv. Data Analysis

3 Credits · Level: Graduate, Undergraduate

This course builds on the material covered in ECON-554 (Data Analysis). Its aim is to introduce students to a set of tools for modeling and understanding complex datasets. This is a recently developed area in statistics and econometrics which blends with parallel developments in computer science and, in particular, machine learning. The course encompasses many methods for supervised and unsupervised learning, including ridge regression, the lasso and other shrinkage methods; kernel smoothing and local fitting; additive models; neural networks; simulation and resampling methods; model assessment and model selection; model averaging; classification and regression trees; support vector machines and flexible discriminants. Hands-on exercises and a group research project using actual economic data represent an integral part of the course.