Quantile Regression on Quantile Ranges – A Threshold Approach
研究了阈值变量不同分位数范围内条件分布可能变化的时间序列模型，推导了阈值参数估计的极限分布，并构造了置信区间，通过模拟验证了覆盖概率。
We study, via quantile regression, time series models whose conditional distribution may change over different quantile range of a threshold variable. We derive the limiting distribution of the estimated threshold parameter under the frameworks of asymptotically shrinking and fixed regime change magnitude. We construct confidence intervals for the estimated threshold parameter via a likelihood‐ratio‐type statistic and tabulate critical values, and by extensive simulation, we investigate their coverage probabilities. We also derive the Bahadur representation allowing for serially correlated errors and discuss related inference problems on threshold effects. Our asymptotic and simulation results complement the existing literature of Caner (2002), Galvao et al (2011, 2014) on threshold regression models.