4 papers
Kernel Density Machines
Andrea Della Vecchia, Damir Filipovic, Paul Schneider
We introduce kernel density machines (KDM), an agnostic kernel-based framework for learning the Radon-Nikodym derivative (density) between probability measures under minimal assump…
The Wisdom of Deliberating AI Crowds: Does Deliberation Improve LLM-Based Forecasting?
Paul Schneider, Amalie Schramm
Structured deliberation has been found to improve the performance of human forecasters. This study investigates whether a similar intervention, i.e. allowing LLMs to review each ot…
Joint Estimation of Conditional Mean and Covariance for Unbalanced Panels
Damir Filipovic, Paul Schneider
We develop a nonparametric, kernel-based joint estimator for conditional mean and covariance matrices in large and unbalanced panels. The estimator is supported by rigorous consist…
Fundamental properties of linear factor models
Damir Filipovic, Paul Schneider
We study conditional linear factor models in the context of asset pricing panels. Our analysis focuses on conditional means and covariances to characterize the cross-sectional and…