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Christian B. Hansen

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University of St.Gallen
University of Chicago Booth

Course location

University of St.Gallen

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University of Chicago Booth
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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 to the Wallace W. Booth professorship in 2014. Hansen graduated from Brigham Young University with a bachelor’s degree in economics in 2000. In 2004, he received a PhD in economics from the Massachusetts Institute of Technology, where he was a graduate research fellow of the National Science Foundation. He joined the Chicago Booth faculty in 2004.

Courses taught by this instructor

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B = Basic
M = Intermediate
A = Advanced

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B = Basic
M = Intermediate
A = Advanced

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Econometrics of Big Data

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.
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A

2023

Econometrics of Big Data

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.
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