Numerical Tools for the Bayesian Analysis of Stochastic Frontier Models

In this paper we describe the use of modern numerical integration methods for making posterior inferences in composed error stochastic frontier models for panel data or individual cross-sections. Two Monte Carlo methods have been used in practical applications. We survey these two methods in some detail and argue that Gibbs sampling methods can greatly reduce the computational difficulties involved in analyzing such models

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Year of publication:	1997
Authors:	Osiewalski, Jacek ; Steel, Mark F. J.
Publisher:	[S.l.] : SSRN
Subject:	Schätztheorie \| Estimation theory \| Stichprobenerhebung \| Sampling \| Kostenfunktion \| Cost function \| Bayes-Statistik \| Bayesian inference

Extent:	1 Online-Ressource (17 p)
Type of publication:	Book / Working Paper
Language:	English
Notes:	Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments Undated erstellt
Other identifiers:	10.2139/ssrn.977 [DOI]
Classification:	C11 - Bayesian Analysis ; C15 - Statistical Simulation Methods; Monte Carlo Methods
Source:	ECONIS - Online Catalogue of the ZBW

Persistent link: https://www.econbiz.de/10014051870