Heterogeneous Endogenous Effects in Networks

• Departmental Seminar

Date and Location

Monday, January 30, 2017, 10:30 AM - 11:50 AM
ARE Library Conference Room, 4101 Social Sciences and Humanities

Abstract

This paper proposes new spatial autoregression models (SARs) allowing individual specific endogenous effects. This model can be estimated using a two-stage LASSO estimator. Existing SARs implicitly assume that each individual in the network has the same endogenous effects on others. However, some individuals are more influential than others. For example, Banerjee et al. (2013) documents that individuals directly connected with some village leaders are more likely to join a micro-finance program than those connected to someone else. I develop a SAR model that allows for individual-specific endogenous effects and propose a two-stage LASSO (2SLSS) procedure to identify influential individuals in a network. Under an assumption of sparsity only a subset of individuals (which can increase with sample size n) is influential I show that my 2SLSS estimator for individual-specific endogenous effects is consistent and achieves asymptotic normality. I also develop robust inference including uniformly valid confidence intervals. These results also hold in scenarios where the influential individuals are not sparse. I extend the analysis to allow for multiple types of connections (multiple networks), and I show how to use the square-root sparse group LASSO to detect which of the multiple connection type is more influential. Simulation evidence shows that my estimator has a good finite sample performance. Application of my method to the data in Banerjee et al. (2013) shows that my proposed procedure can identify leaders and effective networks.