High-dimensional Statistical Learning in Economics and Socioeconomics
Aktuelle Forschungsprojekte (in Arbeit)
Studie von: Österreichisches Institut für Wirtschaftsforschung – Technische Universität Wien – Wirtschaftsuniversität Wien
Auftraggeber: Fonds zur Förderung der wissenschaftlichen Forschung
Recent years have seen a tremendous surge in the availability of socioeconomic data characterised by vast complexity and high
dimensionality. However, prevalent methods employed to inform practitioners and policy makers are still focused on small to
medium-scale datasets. Consequently, crucial transmission channels are easily overlooked and the corresponding inference often
suffers from omitted variable bias. This calls for novel methods which enable researchers to fully exploit the ever increasing
amount of data.In this project, we aim to investigate how the largely separate research streams of Bayesian econometrics,
statistical model checking, and machine learning can be combined and integrated to create innovative and powerful tools for
the analysis of big data in economics and other social sciences. Thereby, we pay special attention to properly incorporating
relevant sources of uncertainty. Albeit crucial for thorough empirical analyses, this aspect is often overlooked in traditional
machine learning techniques which have mainly been centered on producing point forecasts for key quantities of interest only.
In contrast, Bayesian statistics and econometrics are based on designing algorithms to carry out exact posterior inference
which in turn allows for density forecasts. Our contributions are twofold: From a methodological perspective, we develop cutting-edge
methods that enable fully probabilistic inference of dynamic models in vast dimensions. In terms of empirical advances, we
apply these methods to highly complex datasets that comprise situations where either the number of observations, the number
of potential time series and/or the number of variables included is large. More specifically, empirical applications center
on four topical issues in the realm of sustainable development and socioeconomic policy to answer questions such as: How do
market and economic uncertainty affect income inequality? What are the relationships between greenhouse gas emissions and
macroeconomic indicators? Which role do tweets play in the evolution of the prices of crypto-currencies? Which policy measures
are most effective to foster sustainable urban mobility patterns?
Forschungsbereich:Regionalökonomie und räumliche Analyse
