47 papers
This paper proposes a Neyman-orthogonal estimator for linear multidimensional panel data with interactive fixed-effects that combines factor model methods with a weighted-within transformation to achieve parametric consistency and asymptotic normality, demonstrated through an application to beer demand elasticity.
This paper introduces a principled framework for testing excessive influence in linear least-squares models by deriving exact influence formulas and their corresponding extreme value distributions, thereby enabling rigorous hypothesis tests to distinguish between natural sampling variation and problematic data subsets across various scientific domains.
This paper establishes that score-driven updates are uniquely characterized by their ability to reduce the expected Kullback-Leibler divergence relative to the true data-generating density, providing a rigorous information-theoretic foundation that holds even in non-concave, multivariate, and misspecified settings where alternative performance measures fail.
The paper introduces LX Topic, a novel neural topic modeling method that integrates large language model refinement with FASTopic to produce standardized, interpretable, and high-quality document-level topic proportions, thereby establishing a robust and reproducible measurement instrument for business research.
This paper demonstrates that static machine learning-based anti-money laundering enforcement systems in cryptocurrency markets suffer from significant regulatory losses due to temporal nonstationarity and miscalibrated decision rules, rather than declining predictive accuracy, thereby highlighting the need for dynamic, loss-based evaluation frameworks.
This paper proposes a pipeline that leverages large language models to generate documents, extract and canonicalize events, and apply causal discovery methods to produce an inspectable set of plausible causal hypotheses reflecting the models' internal knowledge, rather than guaranteeing real-world causality.
This paper introduces a multi-agent influence diagram framework to unify hybrid threat modeling methods, enabling the evaluation of various countermeasures' effectiveness in dissuading adversaries and mitigating impacts through a simulation of strategic interactions over critical infrastructure cyber attacks.