One-Step and Two-Step Estimation of the Effects of Exogenous Variables on Technical Efficiency Levels

Hung-Jen Wang () and Peter Schmidt

Journal of Productivity Analysis, 2002, vol. 18, issue 2, 129-144

Abstract: Consider a stochastic frontier model with one-sided inefficiency u, and suppose that the scale of u depends on some variables (firm characteristics) z. A “one-step” model specifies both the stochastic frontier and the way in which u depends on z, and can be estimated in a single step, for example by maximum likelihood. This is in contrast to a “two-step” procedure, where the first step is to estimate a standard stochastic frontier model, and the second step is to estimate the relationship between (estimated) u and z. In this paper we propose a class of one-step models based on the “scaling property” that u equals a function of z times a one-sided error u * whose distribution does not depend on z. We explain theoretically why two-step procedures are biased, and we present Monte Carlo evidence showing that the bias can be very severe. This evidence argues strongly for one-step models whenever one is interested in the effects of firm characteristics on efficiency levels. Copyright Kluwer Academic Publishers 2002

Keywords: technical efficiency; stochastic frontiers (search for similar items in EconPapers)
Date: 2002
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (495)

Downloads: (external link)
http://hdl.handle.net/10.1023/A:1016565719882 (text/html)
Access to full text is restricted to subscribers.

Related works:
Working Paper: One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels (2002)
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:kap:jproda:v:18:y:2002:i:2:p:129-144

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/11123/PS2

DOI: 10.1023/A:1016565719882

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 () and Springer Nature Abstracting and Indexing ().