Duong Trinh, Santiago Montoya-Blandón
arXiv 23 Jun 2026 · Econometrics
arXiv:2606.24850 · PDF · DOI · OpenAlex · Extracted main text
This paper introduces a new econometric framework for modeling social interactions with heterogeneous peer responses, addressing endogenous link formation. Our Selection-corrected Heterogeneous Spatial Autoregressive (SCHSAR) approach jointly models link formation and outcome determination. We incorporate a finite mixture structure to capture heterogeneity in peer effects and account for unobserved individual-specific factors driving both network formation and outcome equations, addressing network endogeneity for credible estimation of heterogeneous spillover effects. We propose a fully Bayesian data augmentation approach for estimation and inference, overcoming challenges posed to standard likelihood-based methods. A simulation study validates our approach. Our empirical application to an innovation network among U.S. firms reveals significant positive, yet heterogeneous, peer effects on corporate R&D investments, after accounting for endogenous network formation. The findings highlight varying firm behaviors in response to exogenous R&D policy shocks and and quantify firm-level direct and spillover effects, offering valuable insights for evidence-based and targeted policy design.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Goldsmith-Pinkham, P., and Imbens, G. W (2013) Social networks and the identification of peer effects | 0.874 | 5 | 2 | 100% |
| 2 | Cornwall, G. J., and Parent, O (2017) Embracing heterogeneity: The spatial autoregressive mixture model | 0.811 | 4 | 2 | 100% |
| 3 | Hsieh, C.-S., and Lee, L. F (2016) A social interactions model with endogenous friendship formation and selectivity | 0.811 | 4 | 2 | 100% |
| 4 | Ding, C., Estrada, J., and Montoya-Blandón, S (2023) Bayesian inference of network formation models with payoff externalities self | 0.737 | 3 | 2 | 100% |
| 5 | Johnsson, I., and Moon, H. R (2021) Estimation of peer effects in endogenous social networks: Control function approach | 0.737 | 3 | 2 | 100% |
| 6 | Frühwirth-Schnatter, S (2006) Finite mixture and markov switching models | 0.644 | 2 | 2 | 100% |
| 7 | LeSage, J. P., and Chih, Y.-Y (2016) Interpreting heterogeneous coefficient spatial autoregressive panel models | 0.644 | 2 | 2 | 100% |
| 8 | Andrieu, C., and Thoms, J (2008) A tutorial on adaptive MCMC | 0.511 | 2 | 2 | 50% |
| 9 | Vihola, M (2022) Bayesian inference with adaptive markov chain monte carlo | 0.511 | 2 | 2 | 50% |
| 10 | LeSage, J. P., and Chih, Y.-Y (2018) A bayesian spatial panel model with heterogeneous coefficients | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 68 scored citations.