Bootstrap Inference for Panel Data Quantile Regression
开发了适用于固定效应面板数据分位数回归的Bootstrap推断方法，允许个体内时间依赖，并通过蒙特卡洛模拟和环境库兹涅茨曲线实证验证了方法的有效性。
This article develops bootstrap methods for practical statistical inference in panel data quantile regression models with fixed effects. We consider random-weighted bootstrap resampling and formally establish its validity for asymptotic inference. The bootstrap algorithm is simple to implement in practice by using a weighted quantile regression estimation for fixed effects panel data. We provide results under conditions that allow for temporal dependence of observations within individuals, thus, encompassing a large class of possible empirical applications. Monte Carlo simulations provide numerical evidence the proposed bootstrap methods have correct finite sample properties. Finally, we provide an empirical illustration using the environmental Kuznets curve.