Expected efficiency ranks from parametric stochastic frontier models | Zendy

William C. Horrace | Zendy; Seth RichardsShubik | Zendy; Ian Wright | Zendy

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Expected efficiency ranks from parametric stochastic frontier models

Author(s) -

William C. Horrace,

Seth RichardsShubik,

Ian Wright

Publication year - 2014

Publication title -

empirical economics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.605

H-Index - 56

eISSN - 1435-8921

pISSN - 0377-7332

DOI - 10.1007/s00181-014-0808-8

Subject(s) - econometrics , ranking (information retrieval) , rank (graph theory) , conditional expectation , mathematics , parametric statistics , conditional probability distribution , conditional probability , conditional variance , multivariate statistics , statistics , monte carlo method , economics , computer science , combinatorics , autoregressive conditional heteroskedasticity , volatility (finance) , machine learning

In the stochastic frontier model, we extend the multivariate probability statements of Horrace (J Econom, 126:335–354, 2005) to calculate the conditional probability that a firm is any particular efficiency rank in the sample. From this, we construct the conditional expected efficiency rank for each firm. Compared to the traditional ranked efficiency point estimates, firm-level conditional expected ranks are more informative about the degree of uncertainty of the ranking. The conditional expected ranks may be useful for empiricists. A Monte Carlo study and an empirical example are provided.

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