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Statistical Model Selection and Prediction for Non-standard Data: Insights and Applications in Economics and Finance

Görgen, Konstantin ORCID iD icon 1
1 Institut für Volkswirtschaftslehre (ECON), Karlsruher Institut für Technologie (KIT)

Abstract (englisch):

In an increasingly digital world, data has become abundant and research about leveraging such vast amounts of data is on the rise. While extracting important information relevant
for economic policies or financial risk is crucial, the often non-standard structure of such observational data poses many challenges for researchers. That includes highly correlated,
time-dependent data, combinations of unstructured data, and even high-dimensional situations, where we have very few data points and many potentially relevant factors.
In this thesis, I tackle the above challenges by developing interpretable statistical machine learning methods to reveal important effects of public policies, to better assess risks in
financial applications, and to quantify market drivers. I study causal inference, statistical model selection, and prediction in different social and economic contexts in order to uncover
statistical relationships and to identify important contributing factors.

In the first part of my work, I analyze financial risk with cryptocurrencies and corporate bonds. For the former, I identify classes of assets and time periods where flexible
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Volltext §
DOI: 10.5445/IR/1000149413
Veröffentlicht am 09.08.2022
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Volkswirtschaftslehre (ECON)
Publikationstyp Hochschulschrift
Publikationsdatum 09.08.2022
Sprache Englisch
Identifikator KITopen-ID: 1000149413
Verlag Karlsruher Institut für Technologie (KIT)
Umfang xi, 202 S.
Art der Arbeit Dissertation
Fakultät Fakultät für Wirtschaftswissenschaften (WIWI)
Institut Institut für Volkswirtschaftslehre (ECON)
Prüfungsdatum 28.07.2022
Referent/Betreuer Schienle, Melanie
Conrad, Christian
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