Modeling the Learning from Repeated Samples: A Generalized Cross Entropy Approach
In this study we illustrate a Maximum Entropy (ME) methodology for modeling incomplete information and learning from repeated samples. The basis for this method has its roots in information theory and builds on the classical maximum entropy work of Janes (1957). We illustrate the use of this approach, describe how to impose restrictions on the estimator, and how to examine the sensitivity of ME estimates to the parameter and error bounds. Our objective is to show how empirical measures of the value of information for microeconomic models can be estimated in the maximum entropy view.
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- Miller, D. & Golan, Amos & Judge, G., 1998. "Information Recovery in Simultaneous Equation Statistical Models," Staff General Research Papers 1319, Iowa State University, Department of Economics.
- Golan, Amos & Judge, G. & Miller, D., 1997. "The Maximum Entropy Approach to Estimation and Inference: An Overview," Staff General Research Papers 1327, Iowa State University, Department of Economics.
- Golan, Amos & Judge, George G. & Miller, Douglas, 1996. "Maximum Entropy Econometrics," Staff General Research Papers 1488, Iowa State University, Department of Economics.
- Judge, G. G. & Hill, R. Carter & Bock, M. E., 1990. "An adaptive empirical Bayes estimator of the multivariate normal mean under quadratic loss," Journal of Econometrics, Elsevier, vol. 44(1-2), pages 189-213.
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