A0259
Title: A fixed effect additive stochastic frontier model: A semiparametric estimation of inefficiency determinants
Authors: Taining Wang - Capital University of Economics and Business (China) [presenting]
Feng Yao - West Virginia University (United States)
Abstract: A semiparametric additive stochastic frontier model for panel data is proposed, where inputs and environment variables can enter the frontier individually and interactively through unknown smooth functions. The inefficiency has its mean function known up to certain parameters, and influenced by its determinants that may or may not appear on the frontier. We disentangle time invariant unobserved heterogeneities from inefficiency, which can be helpful to avoid overestimating the inefficiency level. Our model can be identified without the distribution assumption on the composite error, and consistently estimated without suffering from the curse of dimensionality and incidental parameter problems. Thus, our model can include a large number of interested variables as frontier or inefficiency determinants, a feature that can be potentially attractive to empirical studies. We illustrate the appealing finite-sample performance of the proposed estimator and two related hypotheses tests through the Monte Carlo study, and perform an application of world production frontier model with 116 countries during 2001-2013.
