Penalized sieve estimation of zero‐inefficiency stochastic frontiers
针对随机前沿模型中企业效率可能完全有效（零无效率）的情况，提出一种筛型密度估计方法，在非参数设定下估计混合分布，并证明了估计量的一致性和渐近正态性。
Summary Stochastic frontier models for cross‐sectional data typically assume that the one‐sided distribution of firm‐level inefficiency is continuous. However, it may be reasonable to hypothesize that inefficiency is continuous except for a discrete mass at zero capturing fully efficient firms (zero‐inefficiency). We propose a sieve‐type density estimator for such a mixture distribution in a nonparametric stochastic frontier setting under a unimodality‐at‐zero assumption. Consistency, rates of convergence and asymptotic normality of the estimators are established, as well as a test of the zero‐inefficiency hypothesis. Simulations and two applications are provided to demonstrate the practicality of the method.