Robust nonparametric frontier estimation in two steps

We propose a robust methodology for estimating production frontiers with multi-dimensional input via a two-step nonparametric regression, in which we estimate the level and shape of the frontier before shifting it to an appropriate position. Our main contribution is to derive a novel frontier estimation method under a variety of flexible models which is robust to the presence of outliers and possesses some inherent advantages over traditional frontier estimators. Our approach may be viewed as a simplification, yet a generalization, of those proposed by Martins-Filho and coauthors, who estimate frontier surfaces in three steps. In particular, outliers, as well as commonly seen shape constraints of the frontier surfaces, such as concavity and monotonicity, can be straightforwardly handled by our estimation procedure. We show consistency and asymptotic distributional theory of our resulting estimators under standard assumptions in the multi-dimensional input setting. The competitive finite-sample performances of our estimators are highlighted in both simulation studies and empirical data analysis.

Automated summary

We propose a robust methodology for estimating production frontiers with multi-dimensional input via a two-step nonparametric regression, which can handle outliers and shape constraints such as concavity and monotonicity. Our approach is a simplification and generalization of the existing methods and has some inherent advantages. The consistency and asymptotic distributional theory of the resulting estimators are demonstrated under standard assumptions, and their competitive finite-sample performances are highlighted in simulation studies and empirical data analysis.