EMPIRICAL LIKELIHOOD ESTIMATION OF CONDITIONAL MOMENT RESTRICTION MODELS WITH UNKNOWN FUNCTIONS
提出一种基于条件实证似然和筛法的估计方法（SCEL），用于处理含未知函数的条件矩约束模型，证明了参数分量的渐近正态性和有效性，并以含非参数内生性和异方差的部分线性回归模型为例。
This paper proposes an empirical likelihood-based estimation method for conditional moment restriction models with unknown functions, which include several semiparametric models. Our estimator is called the sieve conditional empirical likelihood (SCEL) estimator, which is based on the methods of conditional empirical likelihood and sieves. We derive (i) the consistency and a convergence rate of the SCEL estimator for the whole parameter, and (ii) the asymptotic normality and efficiency of the SCEL estimator for the parametric component. As an illustrating example, we consider a partially linear regression model with nonparametric endogeneity and heteroskedasticity.