ORGANIZER
Priscila Ferreira priscila@eeg.uminho.pt
Phone: +351 253 601 938
STAFF
Susana Figueiredo nipe@eeg.uminho.pt
Phone: +351 253 604 518
| CANCELLED |
In light of the current developments of the Covid-19 crisis, we believe that the prudent course of action is to cancel this year's edition of the NIPE Summer School. We will try to re-schedule the course on the "Econometrics of Big Data" in next year's edition. Further details on the next Summer School of NIPE will be forthcoming. So, please, stay tuned!
Thank you for your interest in our Summer School!
We hope we to hear from you in the future.
In the meantime, take care & stay safe!
Priscila Ferreira
Organiser of the Summer School
About Professor Christian B. Hansen
Christian B. Hansen studies applied and theoretical econometrics, the uses of high-dimensional statistical methods in economic applications, estimation of panel data models, quantile regression, and weak instruments. In 2008, Hansen was named a Neubauer Family Faculty Fellow, and he was named the Wallace W. Booth professorship in 2014.
About the Course
The 17th Edition of the NIPE Summer School in Econometrics will run between June 15 to June 18, 2020. "The Econometrics of Big Data" is the selected topic for this edition, and the course will be taught by Professor Christian B. Hansen, from the University of Chicago - Booth School of Business.
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. Our goal in this course is two-fold. First, we wish to provide an overview and introduction to several modern methods, largely coming from statistics and machine learning, which are useful for exploring high-dimensional data and for building prediction models in high-dimensional settings. Second, we will present 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.
We look forward to welcoming you in Braga!
