Endogenous Environmental Variables In Stochastic Frontier Models
Christine Amsler,
Artem Prokhorov and
Peter Schmidt
No 2017-02, Working Papers from University of Sydney Business School, Discipline of Business Analytics
Abstract:
This paper considers a stochastic frontier model that contains environmental variables that affect the level of inefficiency but not the frontier. The model contains statistical noise, potentially endogenous regressors, and technical inefficiency that follows the scaling property, in the sense that it is the product of a basic (half-normal) inefficiency term and a parametric function of the environmental variables. The environmental variables may be endogenous because they are correlated with the statistical noise or with the basic inefficiency term. Several previous papers have considered the case of inputs that are endogenous because they are correlated with statistical noise, and if they contain environmental variables these are exogenous. One recent paper allows the environmental variables to be correlated with statistical noise. Our paper is the first to allow both the inputs and the environmental variables to be endogenous in the sense that they are correlated either with statistical noise or with the basic inefficiency term. Correlation of inputs or environmental variables with the basic inefficiency term raises non-trivial conceptual issues about the meaning of exogeneity, and technical issues of estimation of the model.
Keywords: environmental variables; stochastic frontier; endogeneity (search for similar items in EconPapers)
Date: 2017-04-09
New Economics Papers: this item is included in nep-dcm, nep-ecm, nep-eff and nep-env
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (49)
Downloads: (external link)
http://hdl.handle.net/2123/16763
Related works:
Journal Article: Endogenous environmental variables in stochastic frontier models (2017) 
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:syb:wpbsba:2123/16763
Access Statistics for this paper
More papers in Working Papers from University of Sydney Business School, Discipline of Business Analytics Contact information at EDIRC.
Bibliographic data for series maintained by Artem Prokhorov ().