Events

Prof. Ming-Yen Cheng
Professor, Department of Mathematics
Director of Statistics Research and Consultancy Centre
Associate Director of Institute for Computational and Theoretical Studies
Hong Kong Baptist University

ABSTRACT

We develop new tests for specification of distribution in stochastic frontier analysis. Particularly, we focus on simultaneous relaxation of two common assumptions: 1) parametric frontier which may lead to fallacious conclusions when misspecified, and 2) homoscedasticity which can be easily violated in real data. While these two issues have been extensively studied in works focusing on estimation of a stochastic frontier and inefficiencies, they have not been properly addressed in the considered testing problems. To this end, we propose novel bootstrap and asymptotic distribution-free tests with neither a parametric frontier nor homoscedasticity assumptions, in both cross-sectional and panel settings. Our tests are asymptotically consistent, simple to implement and widely applicable. Their powers against general fixed alternatives tend to one as sample size increases, and they can detect root-$n$ order local alternatives. We demonstrate their efficacies through extensive simulation studies. When being applied to a banking panel dataset, our tests provide a sound justification for the commonly used exponential specification for banking data, and suggest a new parametric frontier model that is more plausible than the conventional translog frontier.