Nonparametric frontier estimation via local linear regression

In this paper we propose a nonparametric regression frontier model that assumes no specific parametric family of densities for the unobserved stochastic component that represents efficiency in the model. Nonparametric estimation of the regression frontier is obtained using a local linear estimator that is shown to be consistent and nhn asymptotically normal under standard assumptions. The estimator we propose envelops the data but is not inherently biased as free disposal hull-FDH or data envelopment analysis-DEA estimators. It is also more robust to extreme values than the aforementioned estimators. A Monte Carlo study is performed to provide preliminary evidence on the estimator's finite sample properties and to compare its performance to a bias corrected FDH estimator.