A Monte Carlo Study of Efficiency Estimates from Frontier Models

William Horrace and Seth O. Richards

No 97, Center for Policy Research Working Papers from Center for Policy Research, Maxwell School, Syracuse University

Abstract: Parametric stochastic frontier models yield firm-level conditional distributions of inefficiency that are truncated normal. Given these distributions, how should one assess and rank firm-level efficiency? This study compares the techniques of estimated (a) the conditional means of inefficiency and (b) probabilities that firms are most or least efficient. Monte Carlo experiments suggest that the efficiency probabilities are more reliable in terms of mean absolute percent error when inefficiency has large variation across firms. Along the way we tackle some interesting problems associated with simulating and assessing estimator performance inthe stochastic frontier environment.

Keywords: Truncated normal; stochastic frontier; efficiency; multivariate probabilities. (search for similar items in EconPapers)
JEL-codes: C12 C16 C44 D24 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2007-08
New Economics Papers: this item is included in nep-ecm and nep-eff
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Persistent link: https://EconPapers.repec.org/RePEc:max:cprwps:97

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