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

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  • Judge, George
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    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.

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    Bibliographic Info

    Paper provided by Department of Agricultural & Resource Economics, UC Berkeley in its series Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series with number qt5d98g7wg.

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    Date of creation: 20 Apr 2012
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    Handle: RePEc:cdl:agrebk:qt5d98g7wg

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    Keywords: Social and Behavioral Sciences; information theoretic methods; state space models; first order Markov processes; inverse problems; dynamic economic systems.;

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    1. Dennis Kristensen & Yongseok Shin, 2008. "Estimation of Dynamic Models with Nonparametric Simulated Maximum Likelihood," CREATES Research Papers 2008-58, School of Economics and Management, University of Aarhus.
    2. Douglas Miller, 2007. "Behavioral Foundations for Conditional Markov Models of Aggregate Data," Working Papers 0718, Department of Economics, University of Missouri.
    3. 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, 08.
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