论文标题
低排放面板分位回归：估计和推理
Low-rank Panel Quantile Regression: Estimation and Inference
论文作者
论文摘要
在本文中，我们提出了一类低级面板的分位数回归模型，这些模型允许在个人和时间上都没有观察到的斜率异质性。我们通过核标准正则化估计异质截距和斜率矩阵，然后进行样品分裂，行和列的分位数回归和偏差。我们表明，与截距和斜率矩阵相关的因素和因子负荷的估计值在渐变上是正态分布的。此外，我们开发了两项规范测试：一个用于零假设，即斜率系数是随着时间的流逝是一个恒定的，在斜率矩阵的真实等级等级的情况下是一个恒定的，另一个是一个零假设，即斜率系数在添加结构的情况下表现出slope matrix imal等级两者的添加结构。我们通过蒙特卡洛模拟和实际数据集说明了估计和推断的有限样本性能。
In this paper, we propose a class of low-rank panel quantile regression models which allow for unobserved slope heterogeneity over both individuals and time. We estimate the heterogeneous intercept and slope matrices via nuclear norm regularization followed by sample splitting, row- and column-wise quantile regressions and debiasing. We show that the estimators of the factors and factor loadings associated with the intercept and slope matrices are asymptotically normally distributed. In addition, we develop two specification tests: one for the null hypothesis that the slope coefficient is a constant over time and/or individuals under the case that true rank of slope matrix equals one, and the other for the null hypothesis that the slope coefficient exhibits an additive structure under the case that the true rank of slope matrix equals two. We illustrate the finite sample performance of estimation and inference via Monte Carlo simulations and real datasets.
