The Information Theoretic Foundations of a Probabilistic and Predictive Micro and Macro Economics
AbstractDespite 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. ToÂ 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 forÂ understanding and making predictions about dynamic micro and macro economic processes and systems.
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Bibliographic InfoPaper 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.
Date of creation: 20 Apr 2012
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Social and Behavioral Sciences; information theoretic methods; state space models; first order Markov processes; inverse problems; dynamic economic systems.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-05-02 (All new papers)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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- Douglas Miller, 2007. "Behavioral Foundations for Conditional Markov Models of Aggregate Data," Working Papers 0718, Department of Economics, University of Missouri.
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