An Information Theoretic Approach to Understanding the Micro Foundations of Macro Processes

Sofia B. Villas-Boas, George Judge

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.

DOI: 10.4236/tel.[phone]                

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.

Keywords

Empirical Exponential Likelihood; Information Theory

Conflicts of Interest

The authors declare no conflicts of interest.

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.