Title: Penalized Sieve Estimation of Zero-Inefficiency Stochastic Frontiers

Schedule: Dec 14, TH 10:30-11:30 AM

Location: 诚明楼 (Chengming Hall）RM 315

Introduction: 蔡俊，华中科技大学管理学院助理教授。2011年于上海交通大学获工学学士学位，2015年于上海财经大学获数量经济学硕士学位，2020年于美国雪城大学经济系获博士学位。研究的主要方向为效率与生产力分析、政策评价。迄今在 Journal of Applied Econometrics, Econometric Review, Empirical Economics, Journal of Productivity Analysis 等国际知名计量期刊发表多篇论文。主持国家自然科学基金青年项目一项，中央高校自主创新项目一项。

Abstract: 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.