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University of Mannheim

Academic institutioneurope · de
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Research library184linked papers
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Selected work

Representative Papers

Projection Inference for set-identified SVARs

Apr 18, 2025

This paper addresses inference challenges for structural vector autoregressive (SVAR) models under set identification. We propose a projection-based inferential method that simultaneously delivers asymptotic frequentist coverage and robust Bayesian credibility: the Wald ellipsoid for reduced-form parameters is projected onto the structural parameter space to construct joint confidence regions. We establish, for the first time in general stationary SVARs, that this projection method achieves asymptotic 1−α frequentist coverage and robust Bayesian credibility. Moreover, we introduce a posterior-calibrated radius adjustment algorithm that ensures exact robust credibility of 1−α while guaranteeing precise 1−α coverage over the identification set. Theoretically, our work unifies dual guarantees—frequentist and robust Bayesian—within a coherent framework; computationally, it remains efficient and implementable. Empirically, we replicate the Baumeister–Hamilton (2015) labor supply–demand model, demonstrating the method’s tightness and robustness.

15 citations1 influentialRead paper

Flexible Covariate Adjustments in Regression Discontinuity Designs

Jul 16, 2021

To address low covariate efficiency and poor scalability to high-dimensional settings in regression discontinuity (RD) designs, this paper proposes a novel class of covariate-adjusted estimators. The method achieves efficient adjustment by subtracting from the outcome variable an optimal nonparametric prediction function of the covariates. Crucially, it preserves the intrinsic robustness of RD estimation while enabling flexible, data-driven estimation of the adjustment function via modern machine learning tools—including Lasso and random forests. Notably, it is the first RD adjustment framework that simultaneously attains asymptotic variance minimization and compatibility with machine learning estimators. Theoretical analysis confirms that the estimator’s first-order asymptotic properties remain unchanged. Empirical re-analyses demonstrate average standard error reductions of 15–30%. The approach is plug-in, computationally lightweight, and broadly applicable across diverse RD settings.

8 citations2 influentialRead paper

Policy choice in time series by empirical welfare maximization

May 08, 2022

Dynamic multivariate time series pose challenges including time-varying environments, historical dependence, dynamic causal effects, and statistical dependencies. Method: This paper proposes Time-series Empirical Welfare Maximization (T-EWM), the first extension of the empirical welfare maximization framework to dynamic time-series settings. T-EWM employs nonparametric potential outcome modeling and conditional welfare optimization to learn dynamic optimal policies. Contribution/Results: We establish theoretical guarantees, including conditional welfare consistency and a non-asymptotic upper bound on policy regret. In simulation studies and an empirical application to COVID-19 containment policy evaluation, T-EWM significantly improves policy welfare and achieves rapid regret convergence under limited samples. The framework provides a novel paradigm for dynamic decision-making that balances statistical rigor with practical feasibility.

4 citationsRead paper

Confidence over Time: Confidence Calibration with Temporal Logic for Large Language Model Reasoning

Jan 19, 2026

Existing confidence estimation methods for large language models often reduce multi-step reasoning to a single scalar, neglecting the temporal evolution of confidence and rendering them susceptible to superficial factors such as response length. This limitation impedes their ability to distinguish between correctly reasoned answers and confidently asserted but incorrect ones. To address this, this work introduces Signal Temporal Logic (STL) into confidence calibration for the first time. By leveraging discriminative STL specifications, the approach identifies generalizable temporal patterns that differentiate correct from erroneous reasoning trajectories. Furthermore, it proposes a hypernetwork-driven dynamic STL modeling framework that adaptively adjusts confidence evolution rules based on contextual cues. Evaluated across multiple reasoning benchmarks, the method significantly improves the alignment between predicted confidence scores and actual accuracy, outperforming current state-of-the-art baselines.

1 citationsRead paper

Are Synthetic Corruptions A Reliable Proxy For Real-World Corruptions?

May 07, 2025

Real-world distribution shifts—such as weather and illumination variations—severely degrade the robustness of deep learning models. However, collecting diverse, real-world degraded data is prohibitively expensive, prompting widespread reliance on synthetic degradation; yet its fidelity in reflecting real-world degradation effects remains unclear. Method: We construct the largest cross-domain (real vs. synthetic) semantic segmentation corruption benchmark to date, built upon Cityscapes and other datasets using the CorruptIO toolkit. We systematically evaluate 12 corruption types across multiple models and metrics (mIoU, RankCorr). Contribution/Results: We discover, for the first time, a strong correlation (ρ = 0.89) between model performance under real and synthetic corruptions. We further propose a corruption-type-level correlation analysis framework to characterize the applicability boundaries of synthetic degradation. All evaluation code, protocols, and benchmarks are publicly released to advance standardized robustness assessment.

1 citationsRead paper
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