AI 中文总结
本文提出一种成对差分分布回归方法，用于含双向固定效应的二元网络模型，通过二值化结果识别参数，采用条件极大似然估计规避 incidental parameter 问题，经蒙特卡洛模拟验证有效，应用于双边贸易发现贸易壁垒系数分布差异显著。
AI 中文摘要
本文针对双向固定效应随结果阈值变化的二元网络设定，开发了一套分布回归的估计与推断框架。研究表明，通过对每个阈值处的结果进行二值化处理可实现结构参数的识别，采用条件极大似然估计模型，该方法可“差分掉”固定效应并规避 incidental parameter 问题。无论稀疏性源于网络结构还是极端阈值的二值化，该估计量在稀疏性下仍保持渐近无偏。第二个创新点在于，建立了具有不同收敛速率的多阈值估计量的联合渐近分布，并开发了用于检验阈值间系数相等性的联合置信带与检验方法。蒙特卡洛模拟证实，在稀疏性下该方法具有小偏差、有效推断及正确的联合覆盖性。将其应用于双边贸易时发现，系数在分布间存在显著差异，且关键贸易壁垒的相等性假设被拒绝。
英文摘要
I develop an estimation and inference framework for distribution regression in dyadic network settings with two-way fixed effects that vary across thresholds of the outcome. I show that identification of the structural parameters is achieved through binarization of the outcome at each threshold, and estimate the model by conditional maximum likelihood, which "differences out" the fixed effects and circumvents the incidental parameter problem. The estimator remains asymptotically unbiased under sparsity, whether from the network structure or binarization at extreme thresholds. The second novelty is to establish the joint asymptotic distribution of the estimators across multiple thresholds with different convergence rates, and to develop simultaneous confidence bands and tests for equality of coefficients across thresholds. Monte Carlo simulations confirm small bias, valid inference, and correct simultaneous coverage under sparsity. An application to bilateral trade finds that coefficients vary substantially across the distribution, with equality rejected for key trade barriers.
