arXiv:2603.24705v2 Announce Type: replace-cross
Abstract: Discrete choice models are fundamental tools in management science, economics, and marketing for understanding and predicting decision-making. Logit-based models are dominant in applied work, l…
arXiv:2603.29889v2 Announce Type: replace-cross
Abstract: This paper develops a penalized GMM (PGMM) framework for automatic debiased inference on functionals of nonparametric instrumental variable estimators. We derive convergence rates for the PGMM …
arXiv:2604.02403v1 Announce Type: cross
Abstract: This paper establishes the theoretical and practical foundations for using Large Language Models (LLMs) as measurement instruments for latent economic variables — specifically variables that describe …
arXiv:2604.02068v1 Announce Type: new
Abstract: Network analysis of inter-industry payment flows reveals structural economic relationships invisible to traditional bilateral measurement approaches, with significant implications for real-time economic …
arXiv:2509.17180v3 Announce Type: replace
Abstract: Many common estimators in machine learning and causal inference are linear smoothers, where the prediction is a weighted average of the training outcomes. Some estimators, such as ordinary least squa…
arXiv:2603.29972v1 Announce Type: cross
Abstract: Scientists often want to explain why an outcome is different in two groups. For instance, differences in patient mortality rates across two hospitals could be due to differences in the patients themsel…
arXiv:2507.11780v2 Announce Type: replace-cross
Abstract: Constructing confidence intervals for the value of an (unknown) optimal treatment policy is a fundamental problem in causal inference. Insight into the optimal policy value can guide the develo…
arXiv:2603.29889v1 Announce Type: cross
Abstract: This paper develops a penalized GMM (PGMM) framework for automatic debiased inference on functionals of nonparametric instrumental variable estimators. We derive convergence rates for the PGMM estimato…
arXiv:2511.07014v2 Announce Type: replace-cross
Abstract: Probabilistic forecasting is crucial in multivariate financial time-series for constructing efficient portfolios that account for complex cross-sectional dependencies. In this paper, we propose…
arXiv:2603.25509v1 Announce Type: cross
Abstract: We propose a method for constructing distribution-free prediction intervals in nonparametric instrumental variable regression (NPIV), with finite-sample coverage guarantees. Building on the conditional…