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Posterior Simulators in Econometrics

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  • John Geweke

    () (University of Minnesota and Federal Reserve Bank in Minneapolis)

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.

Suggested Citation

  • John Geweke, "undated". "Posterior Simulators in Econometrics," Computing in Economics and Finance 1996 _019, Society for Computational Economics.
  • Handle: RePEc:sce:scecf6:_019
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    Cited by:

    1. Paul Ruud, "undated". "Restricted Least Squares Subject to Monotonicity and Concavity Constraints," Working Papers _007, University of California at Berkeley, Econometrics Laboratory Software Archive.
    2. Otrok, Christopher, 2001. "On measuring the welfare cost of business cycles," Journal of Monetary Economics, Elsevier, vol. 47(1), pages 61-92, February.
    3. John F. Geweke & Michael P. Keane, 1997. "Mixture of normals probit models," Staff Report 237, Federal Reserve Bank of Minneapolis.
    4. J. Geweke & M. Keane, "undated". "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. 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.
    6. Anthony Tay & Kenneth F. Wallis, 2000. "Density Forecasting: A Survey," Econometric Society World Congress 2000 Contributed Papers 0370, Econometric Society.
    7. Christopher Otrok & Charles H. Whiteman, 1996. "Baynesian Leading Indicators: Measuring and Predicting Economic Conditions," Macroeconomics 9610002, EconWPA.
    8. 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.
    9. 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.
    10. 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.

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