Dominik Rehse
Portrait

Head of Junior Research Group
Digital Economy Department

ZEW – Leibniz Centre for
European Economic Research

News

December 2018
Received price for best scientific achievement 2017/2018 at ZEW for research paper “The effects of uncertainty on market liquidity: Evidence from Hurricane Sandy” forthcoming at Journal of Financial Economics
June 2018
Research paper “The effects of uncertainty on market liquidity: Evidence from Hurricane Sandy” was accepted at Journal of Financial Economics
April 2018
Project “Quantifying cross-border transactions on the Bitcoin network” was funded by SEEK
March 2018
Project “Machine learning of optimal interest rate policy” was funded by Deutsche Bundesbank

Expectations, uncertainty & financial markets

Processing information, forming expectations and acting accordingly is an important driver of economic activity and especially financial markets. I am particularly interested in the origins and effects of uncertainty.

Research papers

Unprecedentedness of economic conditions as a source of uncertainty
Work in progress
Using machine learning methods to measure unprecedentedness of economic conditions

Policy reports

ZEW policy brief: The economic effects of uncertainty
Work in progress

New technologies & economic interaction

Technological innovations oftentimes have a significant impact on economic interaction. I am particularly interested in the consequences of (a) advances in artificial intelligence, machine learning and algorithmic decision-making in the context of digital platforms as well as (b) blockchain technology.

Research papers

Quantifying cross-border transactions on the Bitcoin network
Jointly with Peter Buchmann and Frank Brückbauer, work in progress, funded by SEEK
Analysing full graph of on-blockchain transactions and attributing to cross-border transactions
Indirect discrimination in online consumer credit scoring
Work in progress
Protected consumer attributes are indirectly discriminated against by algorithmic credit scoring
Creating a market for uncovering algorithmic misbehavior
Work in progress
Proposing and testing a market design that uncovers unwanted attributes of black-box algorithms, using the example of autonomous driving

Policy reports

ZEW policy brief: The blockchain investment boom
Work in progress

Methods

Innovations in machine learning create opportunities to enhance the economic and econometric toolset. These innovations are not only technical, but also conceptual, particularly for statistical and causal inference.

Research papers

Treatment effect estimation under predictive power considerations
In preparation for submission
Missing common support is not the only problem when estimating counterfactual outcomes
Testing and visualizing conditional covariate balance
In preparation for submission
Using machine learning methods to go beyond univariate comparisons when checking common support
Machine learning of optimal interest rate policy
Jointly with Jesper Riedler, work in progress, funded by Deutsche Bundesbank
Let economic agents learn within economic simulations, using reinforcement learning
Statistical significance as outlier detection
Work in progress
Use machine learning methods to determine statistical significance

Selected policy talks

Learning from tech firms: Higher quality evidence in decision-making
Talk at client event of consumer goods packaging firm, October 2017
Experimental methods should be used much more widely

Advisory committee work

Big Data Working Group of Deutsche Bundesbank
External expert, July 2015 - October 2017
Providing guidance on using machine learning methods and new kinds of data for central banking and banking supervision