Identification of peer effects via a root estimator

- A root estimator is proposed to identify peer effects in a linear-in-means model.
- The identification does not rely on variation of group sizes or intransitivity.
- The root estimator is consistent and asymptotic normal under heteroskedasticity.
- The root estimator performs well in finite samples.

By exploiting the correlation structure of individual outcomes in a network, we show that a carefully constructed root estimator can identify peer effects in linear social interaction models, when identification cannot be achieved via variation of group sizes or intransitivity of network connections. We establish the consistency and asymptotic normality of the root estimator under heteroskedasticity, and conduct Monte Carlo experiments to investigate its finite sample performance.