Robust Bayesian Inference in Stochastic Frontier Models

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...

Hierarchical Bayesian ARX models for robust inference

Gaussian innovations are the typical choice in most ARX models but using other distributions such as the Student’s t could be useful. We demonstrate that this choice of distribution for the innovations provides an increased robustness to data anomalies, such as outliers and missing observations. We consider these models in a Bayesian setting and perform inference using numerical procedures base...

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 ...

Hierarchical Bayesian approaches for robust inference in ARX models

ARX models in which the innovations are described by as Student’s t distributed instead of the typical Gaussian noise has certain advantages. In this paper, we consider such a model in a Bayesian setting and develop numerical procedures based on Markov Chain Monte Carlo methods to perform inference. This model includes automatic order determination by two alternative methods, based on a paramet...