An Information Theoretic Approach to Understanding the Micro Foundations of Macro Processes
Affiliation(s)
Department of Agricultural and Resource Economics, University of California, Berkeley, USA. Department of Agricultural and Resource Economics and Graduate School, University of California, Berkeley, USA.
Department of Agricultural and Resource Economics, University of California, Berkeley, USA. Department of Agricultural and Resource Economics and Graduate School, University of California, Berkeley, USA.
ABSTRACT
In the context of a simple equilibrium macro process we suggest a probability basis for recovering information regarding the unknown and unobservable micro process, and solving the resulting inverse problem.
In the context of a simple equilibrium macro process we suggest a probability basis for recovering information regarding the unknown and unobservable micro process, and solving the resulting inverse problem.
Cite this paper
S. B. Villas-Boas and G. Judge, "An Information Theoretic Approach to Understanding the Micro Foundations of Macro Processes," Theoretical Economics Letters, Vol. 3 No. 1, 2013, pp. 48-51. doi: 10.4236/tel.2013.31008.
S. B. Villas-Boas and G. Judge, "An Information Theoretic Approach to Understanding the Micro Foundations of Macro Processes," Theoretical Economics Letters, Vol. 3 No. 1, 2013, pp. 48-51. doi: 10.4236/tel.2013.31008.
References
[1] A. Gorban and G. Judge, “Entropy the Markov Ordering Approach,” Entropy, 12: 5, 2009,1145-1193. [2] G. Judge and R. Mittelhammer, “An Information Theoretic Approach to Econometrics,” Cambridge University Press, Cambridge, 2012. [3] N. Cressie and T. Read, “Multinomial Goodness of Fit Tests,” Journal of Royal Statistical Society, 46: 3, 1984. 448-464. [4] T. Read and N. Cressie, “Goodness of Fit Statistics for Discrete Multivariate Data,” Springer Verlag, New York, 1988. doi:10.1007/978-1-4612-4578-0 [5] E. Jaynes, “Information Theory and Statistical Mechanics,” In: K. W. Ford, Ed., Statistical Physics, W. A. Benjamin, New York, 1963, pp. 181-218. [6] A. Owen, “Empirical Likelihood,” Chapman and Hall, New York, 2001. doi:10.1201/9781420036152 [7] E. Smith and D. Foley, “Classical Thermodynamics and Economic General Equilibrium Theory,” Journal of Economic Dynamics and Control, 32: 1, 2008,7-65. [8] A. Golan, G. Judge and D. Miller, “Maximum Entropy Econometrics,” John Wiley and Sons, Chichester, 1996.
[1] A. Gorban and G. Judge, “Entropy the Markov Ordering Approach,” Entropy, 12: 5, 2009,1145-1193. [2] G. Judge and R. Mittelhammer, “An Information Theoretic Approach to Econometrics,” Cambridge University Press, Cambridge, 2012. [3] N. Cressie and T. Read, “Multinomial Goodness of Fit Tests,” Journal of Royal Statistical Society, 46: 3, 1984. 448-464. [4] T. Read and N. Cressie, “Goodness of Fit Statistics for Discrete Multivariate Data,” Springer Verlag, New York, 1988. doi:10.1007/978-1-4612-4578-0 [5] E. Jaynes, “Information Theory and Statistical Mechanics,” In: K. W. Ford, Ed., Statistical Physics, W. A. Benjamin, New York, 1963, pp. 181-218. [6] A. Owen, “Empirical Likelihood,” Chapman and Hall, New York, 2001. doi:10.1201/9781420036152 [7] E. Smith and D. Foley, “Classical Thermodynamics and Economic General Equilibrium Theory,” Journal of Economic Dynamics and Control, 32: 1, 2008,7-65. [8] A. Golan, G. Judge and D. Miller, “Maximum Entropy Econometrics,” John Wiley and Sons, Chichester, 1996.