Inference in Stochastic Frontier Models Based on Asymmetry

Semiparametric Bayesian Inference for Stochastic Frontier Models

In this paper we propose a semiparametric Bayesian framework for the analysis of stochastic frontiers and efficiency measurement. The distribution of inefficiencies is modelled nonparametrically through a Dirichlet process prior. We suggest prior distributions and implement a Bayesian analysis through an efficient Markov chain Monte Carlo sampler, which allows us to deal with practically releva...

Stochastic Frontier Models

Endogeneity in Stochastic Frontier Models

Stochastic frontier models are typically estimated by maximum likelihood (MLE) orcorrected ordinary least squares. The consistency of either estimator depends on exogeneity of the explanatory variables (inputs, in the production frontier setting). We will investigate the case that one or more of the inputs is endogenous, in the simultaneous equation sense of endogeneity. That is, we worry that ...

Spatial-temporal Stochastic Frontier Models

The stochastic frontier model with heterogeneous technical efficiency explained by exogenous variables is augmented with a sparse spatial autoregressive component for a crosssection data, and a spatial-temporal component for a panel data. An estimation procedure that takes advantage of the additivity of the model is proposed, computational advantages over simultaneous maximum likelihood estimat...

Inference in stochastic frontier analysis with dependent error terms

Stochastic frontier analysis (SFA) is often used to estimate technical efficiency of entities such as firms, countries or municipalities. A potential dependence between the two components of the error term can be taken into account by a copula function. While estimation of the model is straightforward using the Corrected Ordinary Least Squares (COLS) and Maximum Likelihood (ML) methods, an open...