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The Information Theoretic Foundations of a Probabilistic and Predictive Micro and Macro Economics

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  • Judge, George

Abstract

Despite the productive efforts of economists, the disequilibrium nature of the economic system and imprecise predictions persist. One reason for this outcome is that traditional econometric models and estimation and inference methods cannot provide the necessary quantitative information for the causal influence-dynamic micro and macro questions we need to ask given the noisy indirect effects data we use. To move economics in the direction of a probabilistic and causal based predictive science, in this paper information theoretic estimation and inference methods are suggested as a basis for understanding and making predictions about dynamic micro and macro economic processes and systems.

Suggested Citation

  • Judge, George, 2012. "The Information Theoretic Foundations of a Probabilistic and Predictive Micro and Macro Economics," Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series qt5d98g7wg, Department of Agricultural & Resource Economics, UC Berkeley.
  • Handle: RePEc:cdl:agrebk:qt5d98g7wg
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    References listed on IDEAS

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    1. Kristensen, Dennis & Shin, Yongseok, 2012. "Estimation of dynamic models with nonparametric simulated maximum likelihood," Journal of Econometrics, Elsevier, vol. 167(1), pages 76-94.
    2. Joseph E. Stiglitz, 2011. "Rethinking Macroeconomics: What Failed, And How To Repair It," Journal of the European Economic Association, European Economic Association, vol. 9(4), pages 591-645, August.
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