Title: Inference on social effects when the network is sparse and unknown
Authors: Eric Gautier - Toulouse School of Economics (France) [presenting]
Christiern Rose - University of Queensland (Australia)
Abstract: Models of social interaction are considered when the underlying networks are unobserved but sparse and there are endogenous, contextual, and correlated effects. We accommodate prior knowledge on the sparsity pattern (group sparsity, known existing or nonexisting links) and restrictions on the parameters. We provide results on identification, rates of convergence, model selection, and inference for the parameters and linear functionals in the high-dimensional paradigm. The inference is robust to identification and uniform over large classes of sparse identifiable parameters and data generating processes. Some results hold in finite samples.