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Martin Spindler

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University of St.Gallen
University of Hamburg

Course location

University of St.Gallen

Home university

University of Hamburg
Martin_Spindler
Martin Spindler is Professor of Statistics at the Department of Business Administration (Hamburg Business School) at the University of Hamburg. He studied Economics and Mathematics and holds a Ph.D. from the Ludwig-Maximilians-University in Munich, Germany. Amongst others, he was awarded a research prize of the Geneva Association. His research interests are Machine Learning methods and their application to problems in economics and business administration, in particular finance, insurance and health economics. He also leads the department “Big Data & Digital Health” at the Hamburg Center for Health Economics.

Courses taught by this instructor

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B = Basic
M = Intermediate
A = Advanced

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Course

Description

Instructor

Level

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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