An improved Afriat-Diewert-Parkan nonparametric production function estimator

Ole Bent Olesen, John Ruggiero

Research output: Contribution to journalJournal articleResearchpeer-review

112 Downloads (Pure)


Recent developments in the production frontier literature include nonparametric estimators with shape constraints. A few of these estimators rely on the Afriat inequalities to provide piecewise linear approximations to the production function/frontier. We show in this paper that these Afriat–Diewert–Parkan (ADP) estimators have deficiencies in the presence of moderate statistical noise including overfitting and a relatively high estimator variance. We propose new estimators with lower variance and a relatively low bias. We consider such alternative estimators based on (weighted) averages of random hinge functions with parameter restrictions. Small sample properties of the estimators are presented that show our new estimators outperform the existing ADP estimators when moderate to large amounts of noise are present.

Original languageEnglish
JournalEuropean Journal of Operational Research
Issue number3
Pages (from-to)1172-1188
Publication statusPublished - 1. Feb 2018


  • Concave nonparametric frontier estimators
  • Data envelopment analysis
  • Hinge functions
  • Model averaging
  • Stochastic DEA


Dive into the research topics of 'An improved Afriat-Diewert-Parkan nonparametric production function estimator'. Together they form a unique fingerprint.

Cite this