Expected efficiency ranks from parametric stochastic frontier models

William C. Horrace, Seth Richards-Shubik, Ian Wright

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

In the stochastic frontier model, we extend the multivariate probability statements of Horrace (J Econom, 126:335–354, 2005) to calculate the conditional probability that a firm is any particular efficiency rank in the sample. From this, we construct the conditional expected efficiency rank for each firm. Compared to the traditional ranked efficiency point estimates, firm-level conditional expected ranks are more informative about the degree of uncertainty of the ranking. The conditional expected ranks may be useful for empiricists. A Monte Carlo study and an empirical example are provided.

Original languageEnglish (US)
Pages (from-to)829-848
Number of pages20
JournalEmpirical Economics
Volume48
Issue number2
DOIs
StatePublished - Jan 1 2015
Externally publishedYes

Fingerprint

Stochastic Frontier
firm
efficiency
Point Estimate
ranking
Monte Carlo Study
Conditional probability
uncertainty
Model
Ranking
Uncertainty
Calculate
Stochastic frontier model
Business

Keywords

  • Efficiency estimation
  • Multiplicity
  • Multivariate inference
  • Order statistics

ASJC Scopus subject areas

  • Statistics and Probability
  • Mathematics (miscellaneous)
  • Social Sciences (miscellaneous)
  • Economics and Econometrics

Cite this

Expected efficiency ranks from parametric stochastic frontier models. / Horrace, William C.; Richards-Shubik, Seth; Wright, Ian.

In: Empirical Economics, Vol. 48, No. 2, 01.01.2015, p. 829-848.

Research output: Contribution to journalArticle

Horrace, William C. ; Richards-Shubik, Seth ; Wright, Ian. / Expected efficiency ranks from parametric stochastic frontier models. In: Empirical Economics. 2015 ; Vol. 48, No. 2. pp. 829-848.
@article{7816fc6dc19949b49b2b5bcbcca09487,
title = "Expected efficiency ranks from parametric stochastic frontier models",
abstract = "In the stochastic frontier model, we extend the multivariate probability statements of Horrace (J Econom, 126:335–354, 2005) to calculate the conditional probability that a firm is any particular efficiency rank in the sample. From this, we construct the conditional expected efficiency rank for each firm. Compared to the traditional ranked efficiency point estimates, firm-level conditional expected ranks are more informative about the degree of uncertainty of the ranking. The conditional expected ranks may be useful for empiricists. A Monte Carlo study and an empirical example are provided.",
keywords = "Efficiency estimation, Multiplicity, Multivariate inference, Order statistics",
author = "Horrace, {William C.} and Seth Richards-Shubik and Ian Wright",
year = "2015",
month = "1",
day = "1",
doi = "10.1007/s00181-014-0808-8",
language = "English (US)",
volume = "48",
pages = "829--848",
journal = "Empirical Economics",
issn = "0377-7332",
publisher = "Physica-Verlag",
number = "2",

}

TY - JOUR

T1 - Expected efficiency ranks from parametric stochastic frontier models

AU - Horrace, William C.

AU - Richards-Shubik, Seth

AU - Wright, Ian

PY - 2015/1/1

Y1 - 2015/1/1

N2 - In the stochastic frontier model, we extend the multivariate probability statements of Horrace (J Econom, 126:335–354, 2005) to calculate the conditional probability that a firm is any particular efficiency rank in the sample. From this, we construct the conditional expected efficiency rank for each firm. Compared to the traditional ranked efficiency point estimates, firm-level conditional expected ranks are more informative about the degree of uncertainty of the ranking. The conditional expected ranks may be useful for empiricists. A Monte Carlo study and an empirical example are provided.

AB - In the stochastic frontier model, we extend the multivariate probability statements of Horrace (J Econom, 126:335–354, 2005) to calculate the conditional probability that a firm is any particular efficiency rank in the sample. From this, we construct the conditional expected efficiency rank for each firm. Compared to the traditional ranked efficiency point estimates, firm-level conditional expected ranks are more informative about the degree of uncertainty of the ranking. The conditional expected ranks may be useful for empiricists. A Monte Carlo study and an empirical example are provided.

KW - Efficiency estimation

KW - Multiplicity

KW - Multivariate inference

KW - Order statistics

UR - http://www.scopus.com/inward/record.url?scp=84939887373&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84939887373&partnerID=8YFLogxK

U2 - 10.1007/s00181-014-0808-8

DO - 10.1007/s00181-014-0808-8

M3 - Article

VL - 48

SP - 829

EP - 848

JO - Empirical Economics

JF - Empirical Economics

SN - 0377-7332

IS - 2

ER -