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Estimating Peer Effects Using Partial Network Data

15 Apr 2020

We study the estimation of peer effects through social networks when researchers do not observe the network structure. Instead, we assume that researchers know (have a consistent estimate of) the distribution of the network. We show that this assumption is sufficient for the estimation of peer effects using a linear-in-means model. We present and discuss important examples where our methodology can be applied. In particular, we provide an empirical application to the study of peer effects on students’ academic achievement.
economics science and technology econometrics estimation theory mathematics bias regression cross-validation (statistics) it/computer sciences computing and information technology estimator endogeneity (econometrics) instrumental variables estimation determinant likelihood instrumental variable markov chain monte carlo econometrician cross-validation gibbs sampling expectation–maximization algorithm mcmc law of large numbers
Pages
61
Published in
Québec, QC, CA

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