Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/175504 
Year of Publication: 
2016
Series/Report no.: 
School of Economics Discussion Papers No. 1607
Publisher: 
University of Kent, School of Economics, Canterbury
Abstract: 
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
Subjects: 
High-dimensional models
Social network
Identification
Spatial autoregressive model
GMM
2SLS
regularization methods
Document Type: 
Working Paper

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