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Logical Differencing in Dyadic Network Formation Models with Nontransferable Utilities

Wayne Yuan Gao, Ming Li, Sheng Xu

arXiv 3 Jan 2020 · Econometrics · publishedJournal of Econometrics (2022) · 5 citations (OpenAlex)

arXiv:2001.00691 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper considers a semiparametric model of dyadic network formation under nontransferable utilities (NTU). NTU arises frequently in real-world social interactions that require bilateral consent, but by its nature induces additive non-separability. We show how unobserved individual heterogeneity in our model can be canceled out without additive separability, using a novel method we call logical differencing. The key idea is to construct events involving the intersection of two mutually exclusive restrictions on the unobserved heterogeneity, based on multivariate monotonicity. We provide a consistent estimator and analyze its performance via simulation, and apply our method to the Nyakatoke risk-sharing networks.

Citation extraction

54
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86
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distinct cited
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self-citations
13,690
main-text words

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Gao, W. Y (2020) Nonparametric Identification in Index Models of Link Formation self1.00074100%
2Candelaria, L. E (2016) A Semiparametric Network Formation Model with Multiple Linear Fixed Effects, Working paper, Duke University1.00073100%
3Toth, P (2017) Semiparametric estimation in network formation models with homophily and degree heterogeneity, SSRN 29886981.00073100%
4Gao, W. Y. and M. Li (2021) Robust Semiparametric Estimation in Panel Multinomial Choice Models self0.92843100%
5Graham, B. S (2017) An econometric model of network formation with degree heterogeneity0.87462100%
6Horowitz, J. L (1992) A smoothed maximum score estimator for the binary response model0.5113233%
7Newey, W. and D. McFadden (1994) Large Sample Estimation and Hypothesis Testing, in0.5112250%
8Shi, Z. and X. Chen (2016) A Structural Network Pairwise Regression Model with Individual Heterogeneity0.51121100%
9Bierens, H. J (1983) Uniform Consistency of Kernel Estimators of a Regression Function under Generalized Conditions0.40511100%
10De Weerdt, J. and S. Dercon (2006) Risk-sharing Networks and Insurance against Illness0.40511100%

Showing the top 10 of 54 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1The Econometrics of Utility Transferability in Dyadic Network Formation Models1.000193
2ReLU-Based and DNN-Based Generalized Maximum Score Estimators0.73732
3Identification of Semiparametric Panel Multinomial Choice Models with Infinite-Dimensional Fixed Effects0.64422
4Tractable Identification of Strategic Network Formation Models with Unobserved Heterogeneity0.58531
5A Semiparametric Network Formation Model with Unobserved Linear Heterogeneity0.40511
6Identification and Estimation in a Time-Varying Endogenous Random Coefficient Panel Data Model0.40511
7New possibilities in identification of binary choice models with fixed effects0.40511
8Semiparametric Discrete Choice Models for Bundles0.40511
9Semiparametric Discrete Choice Models for Bundles0.40511
10Identification in Dynamic Dyadic Network Formation Models with Fixed Effects0.40511