Title：A Flexible Semiparametric Stochastic Production Frontier Model with Panel Data

　　Schedule: May 19, TH 2-3 PM

　　Location: 腾讯会议 486 963 847

　　Abstract：We propose a flexible stochastic production frontier model with fixed effects for the panel data in which the semiparametric frontier is additive with bivariate interactions. Instead of maintaining distributional assumptions, we model the conditional mean of the inefficiency to depend on environmental variables and to be known up to a vector of parameters. We propose a difference-based estimator for parameters characterizing the conditional mean of the inefficiency term, a profile series estimator and a kernel-based one-step backfitting estimator for the frontier to facilitate inference. We establish their asymptotic properties, and show that each component in the frontier estimated by the kernel-based backfitting has the same asymptotic distribution as the one estimated with the true knowledge on the other components in the frontier (i.e., the oracle property). Through a Monte Carlo study, we demonstrate that the proposed estimators perform well in finite samples. Utilizing a panel of Chinese firm-level data in 2000-2006,  we apply our method to estimate the frontier and efficiency scores, and conclude that export plays a significant role in reducing the efficiency of firms.

　　Introduction：王钛宁，2019年获得美国西弗吉尼亚大学经济学博士，首都经济贸易大学国际经济管理学学院助理教授，研究方向为非参数计量经济学与随机边界模型。已有研究发表在Econometric Theory, Empirical Economics, The World Economy, Economics Letters  等国际学术期刊。

个人网站 https://694160821.wixsite.com/taining