Pseudolikelihood estimation of the stochastic frontier model

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Stochastic frontier analysis is a popular tool to assess firm performance. Almost universally it has been applied using maximum likelihood (ML) estimation. An alternative approach, pseudolikelihood (PL) estimation, which decouples estimation of the error component structure and the production frontier, has been adopted in both the non-parametric and panel data settings. To date, no formal comparison has yet to be conducted comparing these methods in a standard, parametric cross-sectional framework. We produce a comparison of these two competing methods using Monte Carlo simulations. Our results indicate that PL estimation enjoys almost identical performance to ML estimation across a range of scenarios and performance metrics, and for certain metrics, outperforms ML estimation when the distribution of inefficiency is incorrectly specified.

Original languageEnglish (US)
Pages (from-to)5651-5661
Number of pages11
JournalApplied Economics
Volume49
Issue number55
DOIs
StatePublished - Nov 26 2017

Fingerprint

Pseudo-likelihood
Maximum likelihood estimation
Stochastic frontier model
Inefficiency
Scenarios
Performance metrics
Panel data
Error components
Firm performance
Production frontier
Monte Carlo simulation
Stochastic frontier analysis

Keywords

  • maximum likelihood
  • Monte Carlo simulation
  • production function
  • Stochastic frontier analysis

ASJC Scopus subject areas

  • Economics and Econometrics

Cite this

Pseudolikelihood estimation of the stochastic frontier model. / Andor, Mark; Parmeter, Christopher.

In: Applied Economics, Vol. 49, No. 55, 26.11.2017, p. 5651-5661.

Research output: Contribution to journalArticle

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