Robust nonparametric frontier estimation in two steps

Yining Chen, Hudson S. Torrent and Flavio A. Ziegelmann

Econometric Reviews, 2023, vol. 42, issue 7, 612-634

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.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/07474938.2023.2219183 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:emetrv:v:42:y:2023:i:7:p:612-634

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/LECR20

DOI: 10.1080/07474938.2023.2219183

Access Statistics for this article

Econometric Reviews is currently edited by Dr. Essie Maasoumi

More articles in Econometric Reviews from Taylor & Francis Journals
Bibliographic data for series maintained by ().