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Cited by 958

The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.

Using simulation methods for bayesian econometric models: inference, development,and communication

Open access
Geweke, John·1999·Econometric Reviews

Abstract

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.

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Metadata

Authors
Geweke, J.
Venue
Econometric Reviews
Published
1999-01
Volume
18
Issue
1
Pages
1-73
ISSN
  • 0747-4938 (ISSN-L)
  • 1532-4168
OpenAlex
W1967239989
Cited by
958
References
54
FWCI
22.84
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Using simulation methods for bayesian econometric models: inference, development,and communication. https://app.cochat.ai/papers/10.1080/07474939908800428
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Geweke, J. (1999). Using simulation methods for bayesian econometric models: inference, development,and communication. Econometric Reviews. https://doi.org/10.1080/07474939908800428

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.