Understanding prediction intervals for firm specific inefficiency scores from parametric stochastic frontier models

This paper makes two important contributions to the literature on prediction intervals for firm specific inefficiency estimates in cross sectional SFA models. Firstly, the existing intervals in the literature do not correspond to the minimum width intervals and in this paper we discuss how to compute such intervals and how they either include or exclude zero as a lower bound depending on where the probability mass of the distribution of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$$ u_{i} |\varepsilon_{i} $$</EquationSource> <EquationSource Format="MATHML"> <math xmlns:xlink="http://www.w3.org/1999/xlink"> <mrow> <msub> <mrow> <mi>u</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> <mo lspace="0.15em" rspace="0.15em" stretchy="false">|</mo> <msub> <mrow> <mi mathvariant="italic">ε</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </math> </EquationSource> </InlineEquation> resides. This has useful implications for practitioners and policy makers, with greatest reductions in interval width for the most efficient firms. Secondly, we propose an ‘asymptotic’ approach to incorporating parameter uncertainty into prediction intervals for firm specific inefficiency (given that in practice model parameters have to be estimated) as an alternative to the ‘bagging’ procedure suggested in Simar and Wilson (Econom Rev 29(1):62–98, <CitationRef CitationID="CR27">2010</CitationRef>). The approach is computationally much simpler than the bagging approach. Copyright Springer Science+Business Media New York 2014