Machine-Learning Methods for Economists



Stephen Hansen (University of Oxford)



3-7 September 2018



15:30 to 19:00

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 covering canonical models for regularized regression.  We will then turn to methods for handling unstructured data, with a particular focus on text and other discrete data. While we will review existing methods for information retrieval, the focus will be on more modern and powerful machine-learning approaches. The emphasis throughout 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.



Regularized regression
Information retrieval
Introduction to unsupervised machine learning
Graphical models, Bayesian networks, and latent Dirichlet allocation
Generative models for supervised learning


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 Quarterly Journal of Economics, Review of Economic Studies, and Journal of Monetary Economics.


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