Tchuente, Guy (2019) Weak Identification and Estimation of Social Interaction Models. [Preprint] (doi:10.48550/arXiv.1902.06143) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided) (KAR id:78953)
The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. | |
Official URL: https://doi.org/10.48550/arXiv.1902.06143 |
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: | Preprint |
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DOI/Identification number: | 10.48550/arXiv.1902.06143 |
Refereed: | No |
Other identifier: | https://arxiv.org/abs/1902.06143 |
Name of pre-print platform: | arXiv |
Uncontrolled keywords: | High-dimensional models, Social network, Identification, Spatial autoregressive model, 2SLS, Regularization methods. |
Subjects: | H Social Sciences > HB Economic Theory |
Divisions: | Divisions > Division of Human and Social Sciences > School of Economics |
Depositing User: | Guy Tchuente Nguembu |
Date Deposited: | 22 Nov 2019 13:31 UTC |
Last Modified: | 05 Nov 2024 12:43 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/78953 (The current URI for this page, for reference purposes) |
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