Problem
Research questions and friction points this paper is trying to address.
peer effects
linear-in-means models
extreme peer behavior
quantile social norm
Quantile Social Autoregressive Model
🤖 AI Summary
This study addresses the limitation of linear-in-means models in capturing the influence of extreme peer behaviors by proposing an autoregressive model based on quantile social norms. Methodologically, it introduces a direct estimation mechanism for unknown quantile parameters to identify critical intervals of peer influence. To handle non-smoothness, the approach employs pseudo-response instrumental variable moment conditions combined with kernel smoothing techniques, rigorously establishing the consistency and asymptotic normality of the proposed estimator. Monte Carlo simulations validate the method’s finite-sample performance. An empirical application further demonstrates that specific segments of the distribution exert significant peer effects on individual behavior. Overall, this work provides a novel paradigm for understanding heterogeneous social interactions.
📝 Abstract
Research on modelling peer effects has predominantly relied on linear-in-means models. However, averaging peer responses prevents these models from capturing the effects of extreme peer behavior. To address this limitation, we propose a quantile social autoregressive model based on a novel quantile social norm, which uses empirical quantiles of peer responses. By treating the quantile level as an unknown parameter estimated directly from the data, our approach identifies which segment of the peer response distribution most strongly influences individual behavior. To estimate the model, we introduce new moment conditions using pseudo-response instruments. Because the quantile social norm is nonsmooth, we apply kernel smoothing to the instruments and residuals, ensuring valid statistical inference. Additionally, we establish equilibrium existence and uniqueness and derive the identification conditions. Furthermore, we prove the consistency and asymptotic normality of our proposed estimator. Finally, Monte Carlo experiments examine the finite-sample performance of the estimator, and an empirical application illustrates the interpretation of the estimated quantile levels and their corresponding peer effects.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Quantile Social Autoregressive Model
Peer Effects
Quantile Social Norm
Kernel Smoothing
Pseudo-response Instruments
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