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Methods to identify linear network models: a review

In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper, we review the literature studying econometric methods for the analysis of linear mod...

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Detalles Bibliográficos
Autores principales: Advani, Arun, Malde, Bansi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214266/
https://www.ncbi.nlm.nih.gov/pubmed/30443505
http://dx.doi.org/10.1186/s41937-017-0011-x
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author Advani, Arun
Malde, Bansi
author_facet Advani, Arun
Malde, Bansi
author_sort Advani, Arun
collection PubMed
description In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper, we review the literature studying econometric methods for the analysis of linear models of social effects, a class that includes the ‘linear-in-means’ local average model, the local aggregate model, and models where network statistics affect outcomes. We provide an overview of the underlying theoretical models, before discussing conditions for identification using observational and experimental/quasi-experimental data.
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spelling pubmed-62142662018-11-13 Methods to identify linear network models: a review Advani, Arun Malde, Bansi Swiss J Econ Stat Original Article In many contexts we may be interested in understanding whether direct connections between agents, such as declared friendships in a classroom or family links in a rural village, affect their outcomes. In this paper, we review the literature studying econometric methods for the analysis of linear models of social effects, a class that includes the ‘linear-in-means’ local average model, the local aggregate model, and models where network statistics affect outcomes. We provide an overview of the underlying theoretical models, before discussing conditions for identification using observational and experimental/quasi-experimental data. Springer International Publishing 2018-02-05 2018 /pmc/articles/PMC6214266/ /pubmed/30443505 http://dx.doi.org/10.1186/s41937-017-0011-x Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Article
Advani, Arun
Malde, Bansi
Methods to identify linear network models: a review
title Methods to identify linear network models: a review
title_full Methods to identify linear network models: a review
title_fullStr Methods to identify linear network models: a review
title_full_unstemmed Methods to identify linear network models: a review
title_short Methods to identify linear network models: a review
title_sort methods to identify linear network models: a review
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6214266/
https://www.ncbi.nlm.nih.gov/pubmed/30443505
http://dx.doi.org/10.1186/s41937-017-0011-x
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