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Weak Identification and Estimation of Social Interaction Models

Tchuente, Guy (2019) Weak Identification and Estimation of Social Interaction Models. Working paper. arXiv.org (Unpublished)

Abstract

The identification of the network effect is based on either group size variation, the structure of the network or the relative position in the network. I provide easy-to-verify necessary conditions for the identification of undirected network models based on the number of distinct eigenvalues of the adjacency matrix. Identification of network effects is possible; although in many empirical situations existing identification strategies may require the use of many instruments or instruments that could be strongly correlated with each other. The use of highly correlated instruments or many instruments may lead to weak identification or many instruments bias. This paper proposes regularized versions of the two-stage least squares (2SLS) estimators as a solution to these problems. The proposed estimators are consistent and asymptotically normal. A Monte Carlo study illustrates the properties of the regularized estimators. An empirical application, assessing a local government tax competition model, shows the empirical relevance of using regularization methods.

Item Type: Monograph (Working paper)
Uncontrolled keywords: High-dimensional models, Social network, Identification, Spatial autoregressive model, 2SLS, Regularization methods.
Subjects: H Social Sciences > HB Economic Theory
Divisions: Faculties > Social Sciences > School of Economics
Depositing User: Guy Tchuente
Date Deposited: 22 Nov 2019 13:31 UTC
Last Modified: 05 Feb 2020 16:34 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/78953 (The current URI for this page, for reference purposes)
Tchuente, Guy: https://orcid.org/0000-0001-8507-3337
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