Nonparametric Frontier Modelling: A Novel Inference Approach

In this paper we consider the estimation of a nonparametric frontier model first proposed in Martins-Filho and Yao (2007). We improve their estimation procedure by adopting a variant of the local exponential smoothing introduced in Ziegelmann (2002). Our estimator is shown to be consistent and asymptotically normal under mild regularity conditions. In addition, due to local exponential smoothing, potential negativity of conditional variance functions that may hinder the use of Martins-Filho and Yao's estimator is avoided. A Monte Carlo study is performed to contrast our estimator performance with that of the estimator proposed in Martins-Filho and Yao (2007). We find that there can be significant improvements in finite sample performance when using exponential smoothing in this context.