This paper establishes the distribution of maximally influencial sets in linear models, allowing to test for excessive influence. Read more
Recommended citation: Konrad, L.D., Kuschnig, N. (2026). "Testing Most Influential Sets." International Conference on Learning Representations.
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This paper investigates the effects of subsidies in basic research by means of a general equilibrium model. Read more
Recommended citation: Konrad, L.D. (2024). "Basically Trapped - A General Equilibrium Approach to the Undersupply of Basic Research in Free Markets" arXiv.
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This paper develops a novel Bayesian prior distribution that approximates exact moment conditions while maintaining computational tractability. Read more
This paper analyzes macroeconomic risk by incorporating conditional skewness into growth forecasting models. Read more
This paper attempts to estimate counterfactual deforestation rates for evaluating REDD+ forest conservation programs using machine learning methods. Read more
This paper develops a maximally flexible Bayesian method for detecting structural breaks in panel data when the time dimension is limited. Read more
This paper develops a novel methodology for testing influential subsets in linear regression. Read more
This work introduces an R package for Bayesian indicator saturated models, enabling robust detection of outliers and structural breaks using non-local priors. Read more