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Bayesian and Non-Bayesian Approaches to Scientific Modeling and Inference in Economics and Econometrics

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  • Arnold Zellner

    (University of Chicago)

Abstract

After brief remarks on the history of modeling and inference techniques in economics and econometrics , attention is focused on the emergence of economic science in the 20th century. First, the broad objectives of science and the Pearson-Jeffreys' "unity of science" principle will be reviewed. Second, key Bayesian and non-Bayesian practical scientific inference and decision methods will be compared using applied examples from economics, econometrics and business. Third, issues and controversies on how to model the behavior of economic units and systems will be reviewed and the structural econometric modeling, time series analysis (SEMTSA) approach will be described and illustrated using a macro-economic modeling and forecasting problem involving analyses of data for 18 industrialized countries over the years since the 1950s. Point and turning point forecasting results will be summarized. Last, a few remarks will be made about the future of scientific inference and modeling techniques in economics and econometrics.

Suggested Citation

  • Arnold Zellner, 2000. "Bayesian and Non-Bayesian Approaches to Scientific Modeling and Inference in Economics and Econometrics," Econometric Society World Congress 2000 Contributed Papers 1206, Econometric Society.
  • Handle: RePEc:ecm:wc2000:1206
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    1. Palm, Franz, 1977. "On univariate time series methods and simultaneous equation econometric models," Journal of Econometrics, Elsevier, vol. 5(3), pages 379-388, May.
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    Cited by:

    1. Zellner, Arnold, 2002. "Comments on 'The state of macroeconomic forecasting'," Journal of Macroeconomics, Elsevier, vol. 24(4), pages 499-502, December.

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