Event Type:

Probability Seminar

Date/Time:

Tuesday, May 26, 2009 - 09:00

Location:

Kidder 350

Guest Speaker:

Institution:

Department of Economics, Oregon State

Abstract:

We consider the estimation of a nonparametric stochastic frontier model with composite error density which is known up to a finite parameter vector. Our primary interest is on the estimation of the parameter vector, as it provides the basis for estimation of firm specific (in)efficiency. Our frontier model is similar to that of Fan et al. (1996), but here we extend their work in that: a) we establish the asymptotic properties of their estimation procedure, and b) propose and establish the asymptotic properties of an alternative estimator based on the maximization of a conditional profile likelihood function. The estimator proposed in Fan et al. is asymptotically normally distributed but has bias which does not vanish as the sample size $n ightarrow infty$. In contrast, our proposed estimator is asymptotically normally distributed and correctly centered at the true value of the parameter vector. In addition, our estimator is shown to be efficient in a broad class of semiparametric estimators. A Monte Carlo study is performed to shed light on the finite sample properties of these competing estimators.