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 accessAbstract
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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Cites
Equation of State Calculations by Fast Computing Machines
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., & Teller, E. (1953) · The Journal of Chemical Physics · doi:10.1063/1.1699114 · Cited by 37,192
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
The Structure of Scientific Revolutions: 50th Anniversary Edition
Kuhn, T. S., & Hacking, I. (2012) · doi:10.7208/chicago/9780226458144.001.0001 · Cited by 21,170
Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.
Geman, S., & Geman, D. (1984) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1984.4767596 · Cited by 17,949
Inference from iterative simulation using multiple sequences
Gelman, A., & Rubin, D. B. (1992) · Statistical Science · doi:10.1214/ss/1177011136 · Cited by 16,627
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
Numerical recipes in Pascal: the art of scientific computing
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5158 · Cited by 11,811
Jeffreys, H., Moran, P. A. P., & Chatfield, C. (1922) · Nature · doi:10.1093/oso/9780198503682.001.0001 · Cited by 7,878
Statistical Decision Theory and Bayesian Analysis
Berger, J. O. (1985) · Springer Series in Statistics · doi:10.1007/978-1-4757-4286-2 · Cited by 7,722
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Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach
Smets, F., & Wouters, R. (2007) · American Economic Review · doi:10.1257/aer.97.3.586 · Cited by 4,611
Testing for Indeterminacy: An Application to U.S. Monetary Policy
Lubik, T. A., & Schorfheide, F. (2004) · American Economic Review · doi:10.1257/000282804322970760 · Cited by 868
Bayesian estimation of an open economy DSGE model with incomplete pass-through
Adolfson, M., Laséen, S., Lindé, J., & Villani, M. (2007) · Journal of International Economics · doi:10.1016/j.jinteco.2007.01.003 · Cited by 849
MCMCpack: Markov Chain Monte Carlo in R
Martin, A. D., Quinn, K. M., & Park, J. H. (2011) · Journal of Statistical Software · doi:10.18637/jss.v042.i09 · Cited by 608
Comparing New Keynesian models of the business cycle: A Bayesian approach
Rabanal, P., & Rubio-Ramírez, J. F. (2005) · Journal of Monetary Economics · doi:10.1016/j.jmoneco.2005.08.008 · Cited by 391
Metadata
- Authors
- Geweke, J.
- Venue
- Econometric Reviews
- Publisher
- Informa UK Limited
- 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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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.