Social Interactions Models with Latent Structures
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866917254281560064 |
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| author | Lin, Zhongjian Shi, Zhentao Zheng, Yapeng |
| author_facet | Lin, Zhongjian Shi, Zhentao Zheng, Yapeng |
| contents | This paper studies estimation and inference of heterogeneous peer effects featuring group fixed effects and slope heterogeneity under latent structure. We adapt the Classifier-Lasso algorithm to consistently discover latent structures and determine the number of clusters. To solve the incidental parameter problem in the binary choice model with social interactions, we propose a parametric bootstrap method to debias and establish its asymptotic validity. Monte Carlo simulations confirm strong finite sample performance of our methods. In an application to students' risky behaviors, the algorithm detects two latent clusters and finds that peer effects are significant within one of the clusters, demonstrating the practical applicability in uncovering heterogeneous social interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_06435 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Social Interactions Models with Latent Structures Lin, Zhongjian Shi, Zhentao Zheng, Yapeng Methodology Econometrics This paper studies estimation and inference of heterogeneous peer effects featuring group fixed effects and slope heterogeneity under latent structure. We adapt the Classifier-Lasso algorithm to consistently discover latent structures and determine the number of clusters. To solve the incidental parameter problem in the binary choice model with social interactions, we propose a parametric bootstrap method to debias and establish its asymptotic validity. Monte Carlo simulations confirm strong finite sample performance of our methods. In an application to students' risky behaviors, the algorithm detects two latent clusters and finds that peer effects are significant within one of the clusters, demonstrating the practical applicability in uncovering heterogeneous social interactions. |
| title | Social Interactions Models with Latent Structures |
| topic | Methodology Econometrics |
| url | https://arxiv.org/abs/2602.06435 |