Estimation of Social Interaction Models Using Regularization

44 Pages Posted: 28 Nov 2016

Date Written: November 2016


In social interaction models, the identification of the network effect is based on either group size variation, structure of the network or the relative position in the network measured by the Bonacich centrality measure. These identification strategies imply the use of many instruments or instruments that are highly correlated. The use of highly correlated instruments may lead to the weak identification of the parameters while, in finite samples, the inclusion of an excessive number of moments increases the bias. This paper proposes regularized versions of the 2SLS and GMM as a solution to these problems. The regularization is based on three different methods: Tikhonov, Landweber Fridman, and Principal Components. The proposed estimators are consistent and asymptotically normal. A Monte Carlo study illustrates the relevance of the estimators and evaluates their finite sample performance.

Keywords: High-dimensional models, Social network, Identification, Spatial autoregressive model, GMM, 2SLS, regularization methods.

JEL Classification: C13, C31

Suggested Citation

Tchuente, Guy, Estimation of Social Interaction Models Using Regularization (November 2016). Available at SSRN: or

Guy Tchuente (Contact Author)

University of Kent ( email )

Keynes College, University of Kent
Canterbury, Kent CT2 7NP
United Kingdom

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