Estimating production frontiers and efficiency when output is a discretely distributed economic bad

Eduardo Fé

Research output: Contribution to journalArticle

Abstract

This article studies the estimation of production frontiers and efficiency scores when the commodity of interest is an economic bad with a discrete distribution. Existing parametric econometric techniques (stochastic frontier methods) assume that output is a continuous random variable but, if output is discretely distributed, then one faces a scenario of model misspecification. Therefore a new class of econometric models has been developed to overcome this problem. The Delaporte subclass of models is studied in detail, and tests of hypotheses are proposed to discriminate among parametric models. In particular, Pearson's chi-squared test is adapted to construct a new kernel-based consistent Pearson test. A Monte Carlo experiment evaluates the merits of the new model and methods, and these are used to estimate the frontier and efficiency scores of the production of infant deaths in England. Extensions to the model are discussed.

LanguageEnglish
Pages285-302
Number of pages18
JournalJournal of Productivity Analysis
Volume39
Issue number3
DOIs
Publication statusPublished - Jun 2013

Fingerprint

efficiency
economics
econometrics
commodity
Economics
Production frontier
Production efficiency
infant
scenario
death
experiment
Econometric models
Parametric model
Scenarios
Monte Carlo experiment
England
Model misspecification
Econometrics
Discrete distributions
Commodities

Keywords

  • consistent misspecification test
  • delaporte distribution
  • infant deaths
  • local likelihood
  • Pearson's chi-square tests
  • stochastic frontier

Cite this

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Estimating production frontiers and efficiency when output is a discretely distributed economic bad. / Fé, Eduardo.

In: Journal of Productivity Analysis, Vol. 39, No. 3, 06.2013, p. 285-302.

Research output: Contribution to journalArticle

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