Robust nonparametric frontier estimation in two steps

Chen, Yining ORCID: 0000-0003-1697-1920, S. Torrent, Hudson and A. Ziegelmann, Flavio (2023) Robust nonparametric frontier estimation in two steps. Econometric Reviews, 42 (7). 612 - 634. ISSN 0747-4938

Text (Robust Nonparametric Frontier Estimation in Two Steps) - Accepted Version Repository staff only until 3 July 2024. Download (1MB) | Request a copy

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Abstract

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.

Item Type:	Article
Additional Information:	© 2023 Taylor & Francis Group, LLC
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics H Social Sciences > HB Economic Theory
JEL classification:	C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods: General > C14 - Semiparametric and Nonparametric Methods C - Mathematical and Quantitative Methods > C2 - Econometric Methods: Single Equation Models; Single Variables > C20 - General
Date Deposited:	13 Jun 2023 10:15
Last Modified:	25 Apr 2024 17:30
URI:	http://eprints.lse.ac.uk/id/eprint/119389

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