A0166
Title: Asymptotic failure of peer effects in network regression models
Authors: Keith Levin - University of Wisconsin (United States) [presenting]
Abstract: Network autoregressive models seek to model peer effects, such as contagion and interference, in which node-level responses or behaviors may influence one another. These models are frequently deployed by practitioners in sociology and econometrics, typically in the form of linear-in-means models, in which node-level covariates and local averages of responses are used as predictors. In highly structured networks, previous work has shown that peer effects in linear-in-means models are collinear with other regression terms and thus cannot be estimated, but this collinearity is widely believed to be ignorable, as peer effects are typically identified in empirical networks. A concerning negative result is shown: Under linear-in-means models when node-level covariates are independent of network structure, peer effects become increasingly collinear with other regression terms as the network size (i.e., number of nodes) grows and are inestimable asymptotically. A narrow positive result is also shown: Under certain latent space network models, some peer effects remain identified as the network size grows, albeit under rather stringent conditions. The results suggest that linear models for peer effects are appropriate in far fewer settings than was previously believed.
