Econometrics of Information and Efficiency

Econometrics as an applied discipline attempts to use information in a most efficient manner, yet the information theory and entropy approach developed by Shannon and others has not played much of a role in applied econometrics. "Econometrics of Information and Efficiency" aims to bridge the gap. Broadly viewed, information theory analyzes the uncertainty of a given set of data and its probabilistic characteristics. Whereas the economic theory of information emphasizes the value of information to agents in a market, the entropy theory stresses the various aspects of imprecision of data and their interactions with the subjective decision processes. The tools of information theory, such as the maximum entropy principle, mutual information and the minimum discrepancy are useful in several areas of statistical inference, for instance Bayesian estimation, expected maximum likelihood principle, and the fuzzy statistical regression. This volume analyzes the applications of these tools of information theory to the most commonly used models in econometrics. The features of the text include: a critical survey of the uses of information theory in economics and econometrics; an integration of applied information theory and economic efficiency analysis; and the development of a new economic hypotheses relating information theory to economic growth models. New lines of research are emphasized.