This paper establishes the distribution of maximally influencial sets under the null hypothesis in linear models, allowing to test for excessive influence. Read more
Recommended citation: Konrad, L.D., Kuschnig, N. (2025). "Testing Most Influential Sets." arXiv.
Download Paper
This paper develops a novel methodology for testing influential subsets in linear regression. 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 attempts to estimate counterfactual deforestation rates for evaluating REDD+ forest conservation programs using machine learning methods. Read more
This paper analyzes macroeconomic risk by incorporating conditional skewness into growth forecasting models. Read more
This paper develops a novel Bayesian prior distribution that approximates exact moment conditions while maintaining computational tractability. 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