Using Simulation Methods for Bayesian Econometric Models: Inference, Development, and Communication
نویسنده
چکیده
This paper surveys the fundamental principles of subjective Bayesian inference in econometrics and the implementation of those principles using posterior simulation methods. The emphasis is on the combination of models and the development of predictive distributions. Moving beyond conditioning on a fixed number of completely specified models, the paper introduces subjective Bayesian tools for formal comparison of these models with as yet incompletely specified models. The paper then shows how posterior simulators can facilitate communication between investigators (for example, econometricians) on the one hand and remote clients (for example, decision makers) on the other, enabling clients to vary the prior distributions and functions of interest employed by investigators. A theme of the paper is the practicality of subjective Bayesian methods. To this end, the paper describes publicly available software for Bayesian inference, model development, and communication and provides illustrations using two simple econometric models. *This paper was originally prepared for the Australasian meetings of the Econometric Society in Melbourne, Australia, from July 2–4, 1997. Financial support from National Science Foundation grant SBR-9600040 is gratefully acknowledged, as is fine research assistance from Hulya Eraslan and John Landon-Lane. Siddhartha Chib, William Griffiths, Michael Keane, Dale Poirier, and four anonymous referees provided helpful comments on earlier versions. My understanding of the issues in this paper has greatly benefited from discussions over the years with these and many other individuals, but misconceptions conveyed in this work are entirely my own. The views expressed herein are those of the author and not necessarily those of the Federal Reserve Bank of Minneapolis or the Federal Reserve System. 1997 by John Geweke. This document may be freely reproduced for educational and research purposes provided that (i) this copyright notice is included with each copy, (ii) no changes are made in the document, and (iii) copies are not sold, but retained for individual use or distributed free of charge.
منابع مشابه
Bayesian Inference for Spatial Beta Generalized Linear Mixed Models
In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...
متن کاملCost Analysis of Acceptance Sampling Models Using Dynamic Programming and Bayesian Inference Considering Inspection Errors
Acceptance Sampling models have been widely applied in companies for the inspection and testing the raw material as well as the final products. A number of lots of the items are produced in a day in the industries so it may be impossible to inspect/test each item in a lot. The acceptance sampling models only provide the guarantee for the producer and consumer that the items in the lots are acco...
متن کاملA Bayesian Networks Approach to Reliability Analysis of a Launch Vehicle Liquid Propellant Engine
This paper presents an extension of Bayesian networks (BN) applied to reliability analysis of an open gas generator cycle Liquid propellant engine (OGLE) of launch vehicles. There are several methods for system reliability analysis such as RBD, FTA, FMEA, Markov Chains, and etc. But for complex systems such as LV, they are not all efficiently applicable due to failure dependencies between compo...
متن کاملSimulation based Bayesian econometric inference: principles and some recent computational advances
In this paper we discuss several aspects of simulation based Bayesian econometric inference. We start at an elementary level on basic concepts of Bayesian analysis; evaluating integrals by simulation methods is a crucial ingredient in Bayesian inference. Next, the most popular and well-known simulation techniques are discussed, the Metropolis-Hastings algorithm and Gibbs sampling (being the mos...
متن کاملEconomics 551 - B : ECONOMETRIC METHODS
The first issue is whether one ought to use of Bayesian or Classical methods of inference. I will briefly cover Bayesian methods which have been revitalized given recent developments in monte carlo simulation and numerical integration. Nevertheless, Bayesian methods are still computationally burdensome and heavily linked to particular parametric functional forms, limiting their applicability to...
متن کامل