Penalized sieve estimation of zero-inefficiency stochastic frontiers

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.