Identification and Efficient Estimation of Simultaneous Equations Network Models
Bibliographic Data
| ID | 19182257 |
|---|---|
| Authors | Xiaodong Liu (0000-0002-6115-3049, University of Colorado Boulder, corresponding author) |
| Year | 2014 |
| Volume | 32 |
| Issue | 4 |
| Pages | 516-536 |
| Publication date | 2014-04-24 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Taylor & Francis (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2014.907093 |
| OpenAlex | W1966695653 |
| Language | EN |
| Citations received | 9 |
| References cited | 20 |
This article considers identification and estimation of social network models in a system of simultaneous equations. We show that, with or without row-normalization of the social adjacency matrix, the network model has different equilibrium implications, needs different identification conditions, and requires different estimation strategies. When the adjacency matrix is not row-normalized, the variation in the Bonacich centrality across nodes in a network can be used as an IV to identify social interaction effects and improve estimation efficiency. The number of such IVs depends on the number of networks. When there are many networks in the data, the proposed estimators may have an asymptotic bias due to the presence of many IVs. We propose a bias-correction procedure for the many-instrument bias. Simulation experiments show that the bias-corrected estimators perform well in finite samples. We also provide an empirical example to illustrate the proposed estimation procedure
Adjacency list · Adjacency matrix · Algorithm · Centrality · Econometrics · Estimation · Estimator · Mathematical optimization · Statistics · Complex Network Analysis Techniques · Computer Science · Mathematics · Opinion Dynamics and Social Influence · Social Capital and Networks · Theoretical Computer Science
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| Citations per year | 1 |
|---|---|
| Citation span | 2017 - 2024 (8) |
| Highly cited | No |