The Information Theoretic Foundations of a Probabilistic and Predictive Micro and Macro Economics
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. 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.
|Date of creation:||20 Apr 2012|
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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.
- 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.
- 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.
- Kristensen, Dennis & Shin, Yongseok, 2012. "Estimation of dynamic models with nonparametric simulated maximum likelihood," Journal of Econometrics, Elsevier, vol. 167(1), pages 76-94.
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