Nonparametric Estimation of Nonadditive Random Functions
提出非参数估计方法，用于估计在不可观测随机项中不可加的函数，假设随机项分布未知。当函数满足单调性及经济理论隐含的性质（如齐次性或可分离性）时，可识别函数和随机项分布，并给出估计量的一致性和渐近正态性证明。
We present estimators for nonparametric functions that are nonadditive in unobservable random terms. The distributions of the unobservable random terms are assumed to be unknown. We show that when a nonadditive, nonparametric function is strictly monotone in an unobservable random term, and it satisfies some other properties that may be implied by economic theory, such as homogeneity of degree one or separability, the function and the distribution of the unobservable random term are identified. We also present convenient normalizations, to use when the properties of the function, other than strict monotonicity in the unobservable random term, are unknown. The estimators for the nonparametric function and for the distribution of the unobservable random term are shown to be consistent and asymptotically normal. We extend the results to functions that depend on a multivariate random term. The results of a limited simulation study are presented.