Estimating the confidence intervals for DEA efficiency scores of Asia-Pacific airlines

Data envelopment analysis (DEA) is a popular non-parametric approach to examine performance and productivity of airlines; however, it could not provide statistical information such as confidence intervals on the estimated efficiency scores. We combined stochastic frontier DEA into single framework disentangle noise ‘pure’ inefficiency from scores accordingly for Monte-Carlo simulation verified that our novel model good alternative conventional well bootstrap DEA. Empirical application using Asia-Pacific airlines’ data (2008‒2015) shows after accounting random errors, sampled airlines performed during study period but their was declining, hence, there still room improvement.