Our Masterclasses are designed to be of particular benefit to economists and social scientists in the public and private sectors wanting to know how to use econometric methods and a variety of data to inform policymaking.

They run over two days at the Georgetown University School of Continuing Studies located in downtown Washington, D.C. at 640 Massachusetts Avenue N.W. (unless otherwise noted).

The price for each of the courses is as follows: Students from Higher Education Institutions $500; Faculty and Staff from Higher Educational Institutions $950; Government and nonprofit organization employees $950; All others $1750.

In order for you to attend one of our courses, you must register and submit payment through Eventbrite. If you only register for the course and do not submit payment, your seat won’t be guaranteed until you provide payment. 

Spring 2020

March 26-27, 2020
Christian B. Hansen, University Chicago Booth School of Business
Econometric Methods for High-Dimensional Data

Course Description

As in many other fields, economists are increasingly making use of high-dimensional models – models with many unknown parameters that need to be inferred from the data.  Such models arise naturally in modern data sets that include rich information for each unit of observation (a type of “big data”) and in nonparametric applications where researchers wish to learn, rather than impose, functional forms. High-dimensional models provide a vehicle for modeling and analyzing complex phenomena and for incorporating rich sources of confounding information into economic models.    

Course Intended Audience

Applied statisticians and empirical researchers with an emphasis on those interested in economics and the social sciences more generally, graduate students, researchers who want to quickly get an overview on the theory and applications of econometric/statistical methods for high-dimensional data sets.


  • Present an introduction to recent proposals that adapt high-dimensional methods to the problem of doing valid inference about model parameters and illustrate applications of these proposals for doing inference about economically interesting parameters.
  • Provide an overview of and introduction to popular methods from the statistics and machine learning literature.


  • Introduction to high-dimensional estimation from a traditional nonparametric viewpoint.
  • Key ideas for conducting valid inference using high-dimensional methods.
  • Introduction to common machine learning methods such as penalized estimation, tree-based methods, random forests

Registration and Payment

To register and submit payment for the course, please click on the following link .

Deadline to register and submit the required payment to hold your seat for this course is Friday, March 20, 2020.

This course will be held on the Georgetown University campus in the Old North Building, Room 205 on Thursday, March 26, 2020 and in the Car Barn Building, Room 204 on Friday, March 27, 2020.

If you have any queries, please contact events team at

Previous Masterclasses