Semiparametric stochastic frontier models: A generalized additive model approach

Giancarlo Ferrara and Francesco Vidoli

European Journal of Operational Research, 2017, vol. 258, issue 2, 761-777

Abstract: The choice of the functional form of the frontier into a stochastic frontier model is typically neglected in applications and canonical functions are usually considered. This paper introduces a semiparametric approach for stochastic frontier estimation that extends previous works based on pseudo-likelihood estimators allowing flexibility in model selection and capability of imposing monotonicity and concavity constraints. For these purposes the present work introduces a generalized additive framework that moreover permits to model the influence of contextual/environmental factors to the hypothesized production process by the relative extension given by generalized additive models for location, scale and shape. Through some Monte Carlo simulations and an application to European agricultural data the flexibility of the proposed framework in analyzing efficiency is illustrated.

Keywords: Stochastic frontier; Semiparametric; Generalized additive model; Splines; Efficiency (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (20)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221716307445
Full text for ScienceDirect subscribers only

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:eee:ejores:v:258:y:2017:i:2:p:761-777

DOI: 10.1016/j.ejor.2016.09.008

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().