Machine-Learning Methods for Economists



Stephen Hansen (Oxford University)



28 August – 1 September 2017



9:30 to 13:00

Intended for


Empirical researchers interested in using high-dimensional or unstructured data in their projects.



Knowledge of probability theory equivalent to advanced undergraduate or graduate courses in econometrics. The instructor will circulate voluntary programming exercises that can be done in any scripting language; students will not be required to complete these but they will be discussed in class. Live demonstrations will be in Python.



The course will begin by reviewing existing methods in economics and finance for handling unstructured data, with a particular focus on text. The bulk of the course will then cover more modern and powerful machine-learning approaches. The emphasis will be on probabilistic models, and we will cover both frequentist and Bayesian inference. Software implementations will be discussed for select algorithms, with illustrations in class. While most of the models will be for discrete data, the key ideas are relevant for general machine-learning tasks.



Information retrieval
Introduction to unsupervised machine learning
Graphical models, Bayesian networks, and latent Dirichlet allocation
Supervised learning: penalized and inverse regression


Stephen Hansen is Associate Professor in the Department of Economics at the University of Oxford and a Fellow of University College. He was previously Assistant then Associate Professor of Economics at Pompeu Fabra University after receiving his PhD in Economics from the London School of Economics in 2009. He serves as an academic consultant to the Bank of England, and is affiliated with the Alan Turing Institute, the Centre for Economic Policy Research, and CESifo. His research has been published in leading international journals, including the Review of Economic Studies, Journal of Monetary Economics, and Journal of International Economics.


© CEMFI. All rights reserved.
Our website uses cookies to analyze the navigation of our users. If you continue browsing this site, you are accepting their use. Our Cookies Policy page contains more information about cookies, how we use them, and how to block them through the settings of your browser.