Pseudolikelihood estimation of the stochastic frontier model

dc.contributor.author	Andor, Mark
dc.contributor.author	Parmeter, Christopher
dc.date.accessioned	2016-02-01T12:36:27Z
dc.date.available	2016-02-01T12:36:27Z
dc.date.issued	2016
dc.description.abstract	Stochastic frontier analysis is a popular tool to assess firm performance. Almost universally it has been applied using maximum likelihood estimation. An alternative approach, pseudolikelihood estimation, which decouples estimation of the error component structure and the production frontier, has been adopted in several advanced settings. To date, no formal comparison has yet to be conducted comparing these methods in a standard, parametric cross sectional framework. We seek to produce a comparison of these two competing methods using Monte Carlo simulations. Our results indicate that pseudolikelihood estimation enjoys almost identical performance to maximum likelihood estimation across a range of scenarios, and out performs maximum likelihood estimation in settings where the distribution of inefficiency is incorrectly specified.	en
dc.identifier.uri	http://hdl.handle.net/2003/34486
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-16539
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB 823;7, /2016	en
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.title	Pseudolikelihood estimation of the stochastic frontier model	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access

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