删失分位数回归模型中的变点检测问题

Testing for change points in censored quantile regression models

本文针对固定删失分位数回归模型中的变点问题提出一种新的检测方法;基于观测值的有效子集信息和分位数目标函数的次梯度提出检验统计量.在原假设下,本文得到检验统计量的渐近性质,并且通过模拟方法得到渐近分布的临界值.由于本文提出的方法仅需要在原假设下拟合模型,所以其在计算上更加有效.此外,相比较于传统的Powell方法,数值模拟研究发现本文提出的方法在有限样本的条件下有相近功效及更高的计算效率.最后,本文分析了一组美国居民收入数据集来展示所提方法的实际应用表现.

We develop a new method to test structural changes in quantile regression in the presence of fixed censoring. The testing procedure is based on the observations in an informative subset and is conducted with the subgradient of the quantile objective function, which only requires estimating the model under the null hypothesis.The proposed method is easy to understand and convenient to compute. We establish the limiting distribution of the test statistic under the null hypothesis, and show that its asymptotic critical values can be obtained via simulation methods. Simulation studies show that the proposed method has competitive performance but higher computational efficiency compared with the Powell-based method in finite samples. In addition, an empirical application to the American earnings gap is used to illustrate the performance of the proposed method.