发表机构
Renmin University of China; Southwestern University of Finance and Economics; University of California, Davis（中国人民大学; 西南财经大学; 加州大学戴维斯分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对线性均值模型无法捕捉极端同伴行为影响的问题，提出基于分位数社会规范的分位数社会自回归模型，通过数据估计分位数水平并引入核平滑矩条件，实现有效推断与实证应用。
AI 中文摘要
关于同伴效应建模的研究主要依赖于线性均值模型。然而，对同伴反应取平均会使这些模型无法捕捉极端同伴行为的影响。为解决这一局限，我们提出了一种基于新颖分位数社会规范的分位数社会自回归模型，该规范使用同伴反应的经验分位数。通过将分位数水平视为直接从数据中估计的未知参数，我们的方法能够识别同伴反应分布中哪个部分对个体行为影响最强。为估计该模型，我们引入了使用伪响应工具的新矩条件。由于分位数社会规范是非光滑的，我们对工具和残差应用核平滑，以确保有效的统计推断。此外，我们建立了均衡的存在性和唯一性，并推导了识别条件。进一步，我们证明了所提出估计量的一致性和渐近正态性。最后，蒙特卡洛实验检验了估计量的有限样本性能，实证应用说明了估计分位数水平及其相应同伴效应的解释。
英文摘要
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.
