Network and panel quantile effects via distribution regression
提出一种构建非线性网络和面板模型中分位数函数及分位数效应同时置信带的方法，基于固定效应分布回归估计量的投影，并修正了伴随参数问题，适用于严格外生协变量和离散结果变量。
This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome variables. The method is based upon projection of simultaneous confidence bands for distribution functions constructed from fixed effects distribution regression estimators. These fixed effects estimators are debiased to deal with the incidental parameter problem. Under asymptotic sequences where both dimensions of the data set grow at the same rate, the confidence bands for the quantile functions and effects have correct joint coverage in large samples. An empirical application to gravity models of trade illustrates the applicability of the methods to network data.