Luis E. Candelaria, Yichong Zhang
arXiv 20 Mar 2024 · Econometrics
arXiv:2403.13725 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces a methodology to conduct robust inference in bipartite networks under local misspecification. We focus on a class of dyadic network models with misspecified conditional moment restrictions. The framework of misspecification is local, as the effect of misspecification varies with the sample size. We utilize this local asymptotic approach to construct a robust estimator that is minimax optimal for the mean square error within a neighborhood of misspecification. Additionally, we introduce bias-aware confidence intervals that account for the effect of the local misspecification. These confidence intervals have the correct asymptotic coverage for the true parameter of interest under sparse network asymptotics. Monte Carlo experiments demonstrate that the robust estimator performs well in finite samples and sparse networks. As an empirical illustration, we study the formation of a scientific collaboration network among economists.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Graham, B. S (2022) Sparse network asymptotics for logistic regression | 0.965 | 10 | 5 | 90% |
| 2 | Armstrong, T. B. and M. Kolesár (2021) Sensitivity analysis using approximate moment condition models | 0.874 | 9 | 2 | 100% |
| 3 | Bonhomme, S. and M. Weidner (2022) Minimizing sensitivity to model misspecification | 0.874 | 8 | 2 | 100% |
| 4 | Andrews, I., M. Gentzkow, and J. M. Shapiro (2017) Measuring the sensitivity of parameter estimates to estimation moments | 0.811 | 4 | 2 | 100% |
| 5 | Hsieh, C.-S., M. Konig, X. Liu, and C. Zimmermann (2022) Collaboration in bipartite networks, Tech | 0.811 | 4 | 2 | 100% |
| 6 | Conley, T. G., C. B. Hansen, and P. E. Rossi (2012) Plausibly exogenous | 0.737 | 3 | 2 | 100% |
| 7 | Goyal, S., M. J. Van Der Leij, and J. L. Moraga-González (2006) Economics: An emerging small world | 0.737 | 3 | 2 | 100% |
| 8 | Anderson, K. A. and S. Richards-Shubik (2022) Collaborative production in science: An empirical analysis of coauthorships in economics | 0.644 | 2 | 2 | 100% |
| 9 | Andrews, I., M. Gentzkow, and J. M. Shapiro (2020) On the informativeness of descriptive statistics for structural estimates | 0.644 | 2 | 2 | 100% |
| 10 | Armstrong, T. B., M. Weidner, and A. Zeleneev (2022) Robust Estimation and Inference in Panels with Interactive Fixed Effects | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 51 scored citations.