Research

This paper presents an efficient algorithm to detect maximally influencial in linear regression and proposes consistency criteria for this to hold in double/debiased ML. Read more

This paper establishes the distribution of maximally influencial sets in linear models, allowing to test for excessive influence. Read more

We introduce a maximally flexible Bayesian method for detecting structural breaks in panel data with applications in climate policy. Read more

This paper investigates the effects of subsidies in basic research by means of a general equilibrium model. Read more

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 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