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Penalized sieve estimation of zero‐inefficiency stochastic frontiers
Jun Cai (Huazhong University of Science and Technology), William C. Horrace (Syracuse University), Christopher F. Parmeter (University of Miami)
Abstract: 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.