LASSO for Stochastic Frontier Models with Many Efficient Firms

Stochastic Frontier Models

Stationary Points for Parametric Stochastic Frontier Models

The results of Waldman (1982) on the Normal-Half Normal stochastic frontier model are generalized using the theory of the Dirac delta (Dirac, 1930), and distribution-free conditions are established to ensure a stationary point in the likelihood as the variance of the inefficiency distribution goes to zero. Stability of the stationary point and "wrong skew" results are derived or simulated for c...

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...

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...

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 ...