A bagging-based estimator for dyadic networks with fixed effects attains asymptotic normality and the Cramér-Rao bound for both TU and NTU links by using joint MOM, Le Cam refinement, and split-network jackknife.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
econ.EM 2years
2024 2verdicts
UNVERDICTED 2representative citing papers
A new identification approach distinguishing effort-affecting preference shocks from other GPA shocks yields peer effect estimates 40% higher than GPA-proxy methods when networks include isolated students, shown in US high school data.
citing papers explorer
-
Bagging the Network
A bagging-based estimator for dyadic networks with fixed effects attains asymptotic normality and the Cramér-Rao bound for both TU and NTU links by using joint MOM, Le Cam refinement, and split-network jackknife.
-
Identifying Peer Effects in Networks with Unobserved Effort and Isolated Students
A new identification approach distinguishing effort-affecting preference shocks from other GPA shocks yields peer effect estimates 40% higher than GPA-proxy methods when networks include isolated students, shown in US high school data.