论文标题
Wilks的半参数回归定理，具有弱依赖性数据
Wilks' theorem for semiparametric regressions with weakly dependent data
论文作者
论文摘要
经验可能性推断扩展到一类用于固定，弱依赖性序列的半参数模型。鉴于其过去以及协变量向量的当前和过去值，将部分线性的单个指数回归用于该系列的条件平均值。添加了该系列条件差异的参数模型，以捕获进一步的非线性效应。我们提出了固定数量的合适矩方程，以表征平均值和方差模型。我们得出一个经验对数可能性比率，其中包括多个函数的非参数估计器，我们表明该比率与已知这些函数的情况相同。
The empirical likelihood inference is extended to a class of semiparametric models for stationary, weakly dependent series. A partially linear single-index regression is used for the conditional mean of the series given its past, and the present and past values of a vector of covariates. A parametric model for the conditional variance of the series is added to capture further nonlinear effects. We propose a fixed number of suitable moment equations which characterize the mean and variance model. We derive an empirical log-likelihood ratio which includes nonparametric estimators of several functions, and we show that this ratio has the same limit as in the case where these functions are known.
