The New Palgrave Dictionary of Economics

2018 Edition
| Editors: Macmillan Publishers Ltd

Bayesian Econometrics

  • Dale J. Poirier
Reference work entry
DOI: https://doi.org/10.1057/978-1-349-95189-5_2754

Abstract

‘Bayesian econometrics’ consists of the tools of Bayesian statistics applicable to economic phenomena. The Bayesian paradigm interprets ‘probability’ as a measure of ‘uncertainty’ or ‘degree of belief’ associated with the occurrence of a particular uncertain event, given the available information and any accepted assumptions. It prescribes how an individual should act in the face of such uncertainty in order to avoid undesirable inconsistencies. The coherence of the Bayesian approach contrasts sharply with conventional statistical methods which sometimes advocate negative estimators of positive quantities to ensure unbiasedness, and confidence intervals which may be null or consist of the whole parameter space.

Keywords

Bayes, T Bayes’ theorem Bayesian econometrics Bernoulli, J Collinearity de Finetti, B Empirical Bayes analysis Exchangeability Expected subjective utility Extreme bounds analysis Frequentist statistics Good, I.J Hypothesis testing Interval estimation Jeffreys’ rule Laplace, P.S Likelihood principle Lindley, D Markov chain Monte Carlo methods Maximum likelihood Model building Objective probability Point estimation Prediction Probability Regression Representation theorem Savage, L. J Statistical inference Subjective probability Uncertainty 

JEL Classification

C11 
This is a preview of subscription content, log in to check access

Bibliography

  1. Bauwens, L., M. Lubrano, and J.-F. Richard. 1999. Bayesian inference in dynamic econometric models. Oxford: Oxford University Press.Google Scholar
  2. Berger, J.O., and L.R. Pericchi. 1996. The intrinsic Bayes factor for model selection and prediction. Journal of the American Statistical Association 91: 109–122.CrossRefGoogle Scholar
  3. Berger, J.O., and R.L. Wolpert. 1988. The likelihood principle, 2nd ed. Hayward: Institute of Mathematical Statistics.Google Scholar
  4. Bernardo, J.M. 1979. Reference posterior distributions for Bayesian inference (with discussion). Journal of the Royal Statistical Society, Series B 41: 113–147.Google Scholar
  5. Bernardo, J.M., and A.F.M. Smith. 1994. Bayesian theory. New York: Wiley.CrossRefGoogle Scholar
  6. Gelman, A., J.B. Carlin, H.S. Stern, and D.B. Rubin. 2003. Bayesian data analysis, 2nd ed. New York: Chapman & Hall.Google Scholar
  7. Geweke, J. 2005. Contemporary Bayesian econometrics and statistics. Hoboken: Wiley.CrossRefGoogle Scholar
  8. Jeffreys, H. 1961. Theory of probability, 3rd ed. London: Oxford University Press.Google Scholar
  9. Kadane, J.B., and L.J. Wolfson. 1998. Experiences in elicitation. Statistician 47: 3–19.Google Scholar
  10. Kass, R.E., and A.E. Raftery. 1995. Bayes factors. Journal of the American Statistical Association 90: 773–795.CrossRefGoogle Scholar
  11. Kass, R.E., and L. Wasserman. 1996. The selection of prior distributions by formal rules. Journal of the American Statistical Association 91: 1343–1370.CrossRefGoogle Scholar
  12. Koop, G. 2003. Bayesian econometrics. Chichester: Wiley.Google Scholar
  13. Koop, G., D.J. Poirier, and J. Tobias. 2007. Bayesian econometric methods. In Econometrics exercises series, vol. 7, ed. K. Abadir, J. Magnus, and P.C.B. Phillips. Cambridge: Cambridge University Press.Google Scholar
  14. Lancaster, T. 2004. An introduction to modern Bayesian econometrics. Oxford: Blackwell.Google Scholar
  15. Leamer, E.E. 1978. Specification searches: Ad Hoc inference with nonexperimental data. New York: Wiley.Google Scholar
  16. Leamer, E.E. 1982. Sets of posterior means and bounded variance priors. Econometrica 50: 725–736.CrossRefGoogle Scholar
  17. Poirier, D.J. 1988. Frequentist and subjectivist perspectives on the problems of model building in economics (with discussion). Journal of Economic Perspectives 2(1): 121–170.CrossRefGoogle Scholar
  18. Poirier, D.J. 1995. Intermediate statistics and econometrics: A comparative approach. Cambridge, MA: MIT Press.Google Scholar
  19. Press, S.J., and J.M. Tanur. 2001. The subjectivity of scientists and the Bayesian approach. New York: Wiley.CrossRefGoogle Scholar
  20. Tierney, L., and J.B. Kadane. 1986. Accurate approximations for posterior moments and marginal posterior densities. Journal of the American Statistical Association 81: 82–86.CrossRefGoogle Scholar
  21. Zellner, A. 1971. An introduction to Bayesian inference in econometrics. New York: Wiley.Google Scholar

Copyright information

© Macmillan Publishers Ltd. 2018

Authors and Affiliations

  • Dale J. Poirier
    • 1
  1. 1.