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Research Paper#Econometrics, Network Analysis, Panel Data🔬 ResearchAnalyzed: Jan 3, 2026 08:43

QMLE for Unbalanced Dynamic Network Panel Data

Published:Dec 31, 2025 09:47
1 min read
ArXiv

Analysis

This paper addresses the challenge of estimating dynamic network panel data models when the panel is unbalanced (i.e., not all units are observed for the same time periods). This is a common issue in real-world datasets. The paper proposes a quasi-maximum likelihood estimator (QMLE) and a bias-corrected version to address this, providing theoretical guarantees (consistency, asymptotic distribution) and demonstrating its performance through simulations and an empirical application to Airbnb listings. The focus on unbalanced data and the bias correction are significant contributions.
Reference

“The paper establishes the consistency of the QMLE and derives its asymptotic distribution, and proposes a bias-corrected estimator.”

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Research Paper#Ranking, Statistics, Quasi-Likelihood, U-statistics🔬 ResearchAnalyzed: Jan 3, 2026 16:52

Novel Quasi-Likelihood Framework for Ranking Data

Published:Dec 30, 2025 06:12
1 min read
ArXiv

Analysis

This paper introduces a new quasi-likelihood framework for analyzing ranked or weakly ordered datasets, particularly those with ties. The key contribution is a new coefficient (τ_κ) derived from a U-statistic structure, enabling consistent statistical inference (Wald and likelihood ratio tests). This addresses limitations of existing methods by handling ties without information loss and providing a unified framework applicable to various data types. The paper's strength lies in its theoretical rigor, building upon established concepts like the uncentered correlation inner-product and Edgeworth expansion, and its practical implications for analyzing ranking data.
Reference

“The paper introduces a quasi-maximum likelihood estimation (QMLE) framework, yielding consistent Wald and likelihood ratio test statistics.”

PermalinkArXiv