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Estimation of social interaction models using regularization

Author

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  • Guy Tchuente
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.

Suggested Citation

  • Guy Tchuente, 2016. "Estimation of social interaction models using regularization," Studies in Economics 1607, School of Economics, University of Kent.
    • Handle: RePEc:ukc:ukcedp:1607
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    File URL: https://www.kent.ac.uk/economics/repec/1607.pdf
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    References listed on IDEAS

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    More about this item

    Keywords

    High-dimensional models; Social network; Identification; Spatial autoregressive model; GMM; 2SLS; regularization methods;
    All these keywords.

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