@inbook{cd2620dbadea4422a333f25efeb44dc2,
title = "Stochastic Frontier Models for Discrete Output Variables.",
abstract = "This chapter reviews recent contributions to the area of stochastic frontiers models (SFM) for the analysis of discrete outcomes. More specifically, we discuss models for binary indicators (probit-SFM), ordered categorical data (ordered logit SMF) and discrete outcomes (Poisson SFM). All these models are mixtures of a standard distribution with an asymmetric distribution. This allows us to frame the discussion within a general framework from which most SFM can be derived. Because many of these models might lack a closed form likelihood function, we suggest the use of Maximum Simulated Likelihoods to estimate the parameters of each model. The latter method is easy to implement in a modern computer and the unknown likelihood can be approximated with arbitrary accuracy using low discrepancy sequences such as Halton sequences. ",
author = "Eduardo Fe",
year = "2019",
month = nov,
language = "English",
isbn = "978-3-030-23726-4",
editor = "{ten Raa}, Thijs and William Greene",
booktitle = "The Palgrave Handbook of Economic Performance Analysis",
publisher = "Palgrave Macmillan Ltd",
address = "United Kingdom",
}