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Non-representative sampled networks: Estimation of network structural properties by weighting

Year: 2024
Type: article
Abstract: This paper analyzes statistical issues arising from non-representative samples of a network. Sampled network data could systematically bias the network properties and generate non-classical measurement error problems. Apart from the sampling rate and the elicitation procedure, the biases on network structural measures depend non-trivially on which subpopulations of nodes are missing with higher probability. We propose a methodology, adapting weighted estimators to networked contexts, which enables researchers to recover several network-level statistics and reduce the biases in the estimated network effects. The proposed weighted estimators are consistent and asymptotically normally distributed and have good performance in finite samples. Notably, our approach does not require users to assume any network formation model and is straightforward to implement.
DOI: 10.1016/j.jeconom.2024.105689
Institutions: National Taiwan University, National Central University, Institute of Economics, Academia Sinica, National Chengchi University, Tohoku University, University of the Basque Country, University of West Bohemia in Pilsen, The Ohio State University
Language: English
Open Access status: hybrid
Estimated APC paid: $3,850
Funders: National Science and Technology Council, Grantová Agentura České Republiky, Academia Sinica, Eusko Jaurlaritza, Ministerio de Economía y Competitividad, National Taiwan University, Ministerio de Asuntos Económicos y Transformación Digital, Gobierno de España, European Regional Development Fund, National Institute of Child Health and Human Development
Awards: G100465654, G1360037275, Network diffusion and social cohesion: the role of the clustering coefficient, G5086937834, G534035784, G769014936
OpenAlex ID: W4391716718
FWCI: 1.191
Cites: 70
Cited by: 9
Related to: 10