Empirical Likelihood Confidence Intervals for Nonparametric Nonlinear Nonstationary Regression Models
Empirical Likelihood Confidence Intervals for Nonparametric Nonlinear Nonstationary Regression Models
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发表时间：
2014-12
影响因子：
1.8
通讯作者：
Ryota Yabe
中科院分区：
文献类型：
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作者：
Ryota Yabe
利用经验似然（EL）方法，研究了具有非线性非平稳异质误差的非参数非线性非平稳回归模型的点态置信区间（CI）的构造.众所周知，基于EL的CI具有吸引人的属性，如数据依赖性和自动学生化横截面和弱依赖模型。我们扩展EL理论的非参数非线性非平稳回归模型，并证明了对数EL比收敛到卡方随机变量与一个自由度。这意味着即使协变量遵循非平稳过程，威尔克斯定理也成立。我们还对日本的逆货币需求进行了实证分析，以证明基于EL的CI的数据依赖性。
By using the empirical likelihood (EL), we consider the construction of pointwise confidence intervals (CIs) for nonparametric nonlinear nonstationary regression models with nonlinear nonstationary heterogeneous errors. It is well known that the EL-based CI has attractive properties such as data dependency and automatic studentization in cross-sectional and weak-dependence models. We extend EL theory to the nonparametric nonlinear nonstationary regression model and show that the log-EL ratio converges to a chi-squared random variable with one degree of freedom. This means that Wilks' theorem holds even if the covariate follows a nonstationary process. We also conduct empirical analysis of Japan's inverse money demand to demonstrate the data-dependency property of the EL-based CI.