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

George Judge ()

Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley

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.

Keywords: Social and Behavioral Sciences; information theoretic methods; state space models; first order Markov processes; inverse problems; dynamic economic systems. (search for similar items in EconPapers)
Date: 2012-04-20
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Working Paper: The information theoretic foundations of a probabilistic and predictive micro and macro economics (2012)
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