Name: GCEP Masterclass: "Econometric Methods for High-Dimensional Data"
Start: 2020-03-26T09:00:00-04:00
End: 2020-03-27T17:00:00-04:00
Location: Georgetown University

GCEP Masterclass: "Econometric Methods for High-Dimensional Data"

Julius J. Shapiro, GCEP Program Coordinator

"Econometric Methods for High-Dimensional Data" GCEP Course will be taught by Professor Christian B. Hansen, University of Chicago

Georgetown University

Christian B. Hansen, University of Chicago Booth School of Business will teach the upcoming March 26-27, 2020 GCEP Masterclass &quot;Econometric Methods for High-Dimensional Data&quot; 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. 
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.

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GCEP Masterclass: "Econometric Methods for High-Dimensional Data"

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