This course aims to give an introduction to the application of machine learning to finance, focusing on the problems of portfolio optimization, return prediction, and textual analysis. A particular focus will be on deep learning and the practical details o ...
The course aims to give students the tools to write academic papers and is divided into two parts. The first part covers microeconometric methods including panel data, IVs, difference-in-differences, and regression discontinuity design. The second part cov ...
Students will learn the core concepts and techniques of network analysis with emphasis on causal inference. Theory and
application will be balanced, with students working directly with network data throughout the course. ...
Building up on the basic concepts of sampling, filtering and Fourier transforms, we address stochastic modeling, spectral analysis, estimation and prediction, classification, and adaptive filtering, with an application oriented approach and hands-on numeri ...
The course covers basic econometric models and methods that are routinely applied to obtain inference results in economic and financial applications. ...