Empirical likelihood inference in autoregressive models with time-varying variances
Empirical likelihood inference in autoregressive models with time-varying variances
复制标题
DOI：
10.1080/24754269.2021.1913977
复制
发表时间：
2021-04
影响因子：
0.5
通讯作者：
Yu Han;Chunming Zhang
中科院分区：
文献类型：
--
作者：
Yu Han;Chunming Zhang
本文给出了误差方差由未知非参数时变函数标度的自回归模型参数的经验似然推断方法。与现有的基于非参数和半参数估计的方法相比，该检验统计量避免了对方差函数的估计，同时保持了零值下的渐近卡方分布。模拟研究表明，所提出的方法（a）更稳定，即，与传统的检验统计量相比，（B）更少地依赖于误差方差中的变化点，并且（B）更接近于期望的置信水平。
This paper develops the empirical likelihood ( ) inference procedure for parameters in autoregressive models with the error variances scaled by an unknown nonparametric time-varying function. Compared with existing methods based on non-parametric and semi-parametric estimation, the proposed test statistic avoids estimating the variance function, while maintaining the asymptotic chi-square distribution under the null. Simulation studies demonstrate that the proposed procedure (a) is more stable, i.e., depending less on the change points in the error variances, and (b) gets closer to the desired confidence level, than the traditional test statistic.