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Posterior simulators in econometrics

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  • John F. Geweke

Abstract

Economics is the discipline of using data to revise beliefs about economic issues. In Bayesian econometrics, the revision is conducted in accordance with the laws of probability, conditional on what has been observed. The normative appeal of Bayesian econometrics is the same as that of expected utility maximization and Bayesian learning, the dominant paradigms in economic theory. The questions that econometrics ultimately addresses are similar to those faced by economic agents in models, as well. Given the observed data, what decisions should be made? After bringing data to bear on two alternative models, how is their relative plausibility changed? Any survey of the introductory and concluding sections of papers in the academic literature should provide more examples and illustrate the process of formally or informally updating beliefs. Until quite recently, applied Bayesian econometrics was undertaken largely by those primarily concerned with contributing to the theory, and the proportion of applied work that was formally Bayesian was rather small. There are several reasons for this. First, Bayesian econometrics demands both a likelihood function and a prior distribution, whereas non-Bayesian methods do not. Second, the subjective prior distribution has to be defended, and if the reader (or worse, the editor) does not agree, then the work may be ignored. Third, most posterior moments can't be obtained anyway because the requisite integrals can't be evaluated.

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Bibliographic Info

Paper provided by Federal Reserve Bank of Minneapolis in its series Working Papers with number 555.

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Date of creation: 1995
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Publication status: Published in Advances in economics and econometrics: Theory and applications (Vol. 3, 1997, pp. 128-165)
Handle: RePEc:fip:fedmwp:555

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Keywords: Econometric models;

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Cited by:
  1. Christopher Otrok & Charles H. Whiteman, 1996. "Baynesian Leading Indicators: Measuring and Predicting Economic Conditions," Macroeconomics 9610002, EconWPA.
  2. Susan Athey & Guido W. Imbens, 2007. "Discrete Choice Models With Multiple Unobserved Choice Characteristics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 48(4), pages 1159-1192, November.
  3. Christopher Otrok, 2000. "On Measuring the Welfare Cost of Business Cycles," Econometric Society World Congress 2000 Contributed Papers 1094, Econometric Society.
  4. J. Geweke & M. Keane, . "An Empirical Analysis of Income Dynamics among Men in the PSID: 1968–1989," Institute for Research on Poverty Discussion Papers 1127-97, University of Wisconsin Institute for Research on Poverty.
  5. Gautam Gowrisankaran & Robert J. Town, 2000. "Inferring Hospital Quality from Patient Discharge Records Using a Bayesian Selection Model," Econometric Society World Congress 2000 Contributed Papers 1773, Econometric Society.
  6. Fernández, C. & Osiewalski, J. & Steel, M.F.J., 1996. "On the Use of Panel Data in Bayesian Stochastic Frontier Models," Discussion Paper 1996-17, Tilburg University, Center for Economic Research.
  7. Lutz Kilian & Tao Zha, 2002. "Quantifying the uncertainty about the half-life of deviations from PPP," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(2), pages 107-125.
  8. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
  9. John F. Geweke & Michael P. Keane, 1997. "Mixture of normals probit models," Staff Report 237, Federal Reserve Bank of Minneapolis.
  10. Fernandez, Carmen & Osiewalski, Jacek & Steel, Mark F. J., 1997. "On the use of panel data in stochastic frontier models with improper priors," Journal of Econometrics, Elsevier, vol. 79(1), pages 169-193, July.
  11. Ruud, Paul A., 1995. "Restricted Least Squares Subject to Monotonicity and Concavity Constraints," University of California Transportation Center, Working Papers qt71z2n16p, University of California Transportation Center.

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