Nonparametric least squares methods for stochastic frontier models
Leopold Simar (),
Ingrid Keilegom () and
Additional contact information
Ingrid Keilegom: Université catholique de Louvain
Journal of Productivity Analysis, 2017, vol. 47, issue 3, 189-204
Abstract When analyzing productivity and efficiency of firms, stochastic frontier models are very attractive because they allow, as in typical regression models, to introduce some noise in the Data Generating Process . Most of the approaches so far have been using very restrictive fully parametric specified models, both for the frontier function and for the components of the stochastic terms. Recently, local MLE approaches were introduced to relax these parametric hypotheses. In this work we show that most of the benefits of the local MLE approach can be obtained with less assumptions and involving much easier, faster and numerically more robust computations, by using nonparametric least-squares methods. Our approach can also be viewed as a semi-parametric generalization of the so-called “modified OLS” that was introduced in the parametric setup. If the final evaluation of individual efficiencies requires, as in the local MLE approach, the local specification of the distributions of noise and inefficiencies, it is shown that a lot can be learned on the production process without such specifications. Even elasticities of the mean inefficiency can be analyzed with unspecified noise distribution and a general class of local one-parameter scale family for inefficiencies. This allows to discuss the variation in inefficiency levels with respect to explanatory variables with minimal assumptions on the Data Generating Process.
Keywords: Stochastic frontier analysis; Nonparametric frontiers; Efficiency and productivity analysis; Local polynomial least-squares (search for similar items in EconPapers)
JEL-codes: C1 C14 C13 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5) Track citations by RSS feed
Downloads: (external link)
http://link.springer.com/10.1007/s11123-016-0474-2 Abstract (text/html)
Access to full text is restricted to subscribers.
Working Paper: Nonparametric Least Squares Methods for Stochastic Frontier Models (2014)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:kap:jproda:v:47:y:2017:i:3:d:10.1007_s11123-016-0474-2
Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/11123/PS2
Access Statistics for this article
Journal of Productivity Analysis is currently edited by William Greene, Chris O'Donnell and Victor Podinovski
More articles in Journal of Productivity Analysis from Springer
Bibliographic data for series maintained by Sonal Shukla ().