{"title":"Methods to identify linear network models: a review.","authors":"Arun Advani, Bansi Malde","doi":"10.1186/s41937-017-0011-x","DOIUrl":null,"url":null,"abstract":"<p><p>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 <i>social effects</i>, 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.</p>","PeriodicalId":36872,"journal":{"name":"Swiss Journal of Economics and Statistics","volume":"154 1","pages":"12"},"PeriodicalIF":0.0000,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1186/s41937-017-0011-x","citationCount":"7","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Swiss Journal of Economics and Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s41937-017-0011-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2018/2/5 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 7
Abstract
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.