Economics at your fingertips  

On endogenizing direction vectors in parametric directional distance function-based models

Rolf Färe, Carl Pasurka () and Michael Vardanyan

European Journal of Operational Research, 2017, vol. 262, issue 1, 361-369

Abstract: Empirical studies of production technologies using directional distance functions have traditionally resorted to ad hoc ways of choosing direction vectors for these functions. Yet it is well known that the assumptions placed on the direction vector can have a non-negligible impact on the estimation results. Several recent studies have attempted to address this issue using econometric estimation and Data Envelopment Analysis. We demonstrate the use of parametric nonlinear programming to select the direction vector optimally. Data on the US electric power plants from early 2000s are used to show the difference between results obtained with endogenously determined direction vectors and ad hoc vectors.

Keywords: OR in energy; Nonlinear programming; Directional distance function (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3) Track citations by RSS feed

Downloads: (external link)
Full text for ScienceDirect subscribers only

Related works:
Working Paper: On endogenizing direction vectors in parametric directional distance function-based 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:

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 Dana Niculescu ().

Page updated 2019-05-04
Handle: RePEc:eee:ejores:v:262:y:2017:i:1:p:361-369