Foundations of Info-Metrics: Modeling, Inference, and Imperfect Information

Info-metrics is the science of modeling, reasoning, and drawing inferences under conditions of noisy and insufficient information. It is at the intersection of information theory, statistical inference, and decision-making under uncertainty. It plays an important role in helping make informed decisions even when there is inadequate or incomplete information because it provides a framework to process available information with minimal reliance on assumptions that cannot be validated. In this pioneering book, Amos Golan, a leader in info-metrics, focuses on unifying information processing, modeling and inference within a single constrained optimization framework. Foundations of Info-Metrics provides an overview of modeling and inference, rather than a problem specific model, and progresses from the simple premise that information is often insufficient to provide a unique answer for decisions we wish to make. Each decision, or solution, is derived from the available input information along with a choice of inferential procedure. The book contains numerous multidisciplinary applications and case studies, which demonstrate the simplicity and generality of the framework in real world settings. Examples include initial diagnosis at an emergency room, optimal dose decisions, election forecasting, network and information aggregation, weather pattern analyses, portfolio allocation, strategy inference for interacting entities, incorporation of prior information, option pricing, and modeling an interacting social system. Graphical representations illustrate how results can be visualized while exercises and problem sets facilitate extensions. This book is this designed to be accessible for researchers, graduate students, and practitioners across the disciplines.

Business & Money

Golan, Amos

Economics

Oxford University Press

In this edition of Amos Golan's Maximum Entropy, the problem of recovering and processing information when the underlying data is limited or partial is considered. It surveys a wide range of empirical approaches to this fascinating field. Maximum entropy economics is a relatively new discipline in this area with a growing number of devotees. Statistical economics examines the issues from a sample/population type view point whereas entropy economics looks at these issues as if from a totally dynamic and random system.

Maximum Entropy

Science & Math

John Wiley&Sons, Inc.

In the theory and practice of econometrics the model, the methodand the data are all interdependent links in informationrecovery-estimation and inference. Seldom, however, are theeconomic and statistical models correctly specified, the datacomplete or capable of being replicated, the estimation rulesoptimal and the inferences free of distortion. Faced with theseproblems, Maximum Entropy Economeirics provides a new basis forlearning from economic and statistical models that may benon-regular in the sense that they are ill-posed or underdeterminedand the data are partial or incomplete. By extending the maximumentropy formalisms used in the physical sciences, the authorspresent a new set of generalized entropy techniques designed torecover information about economic systems. The authors compare thegeneralized entropy techniques with the performance of the relevanttraditional methods of information recovery and clearly demonstratetheories with applications including * Pure inverse problems that include first order Markov processes,and input-output, multisectoral or SAM models to * Inverse problems with noise that include statistical modelssubject to ill-conditioning, non-normal errors, heteroskedasticity,autocorrelation, censored, multinomial and simultaneous responsedata, as well as model selection and non-stationary and dynamiccontrol problems Maximum Entropy Econometrics will be of interest to econometricianstrying to devise procedures for recovering information from partialor incomplete data, as well as quantitative economists in financeand business, statisticians, and students and applied researchersin econometrics, engineering and the physical sciences.

Maximum Entropy Econometrics: Robust Estimation with Limited Data

Miller, Douglas

Judge, George G.

wiley

The book shows economists how to estimate the ability of firms to raise their prices above their costs and how to estimate firms' strategies. It is designed for graduate students, researchers, and practitioners. It can be used as a textbook or as a guide to conducting research. No close substitutes exist.

Estimating Market Power and Strategies

Perloff, Jeffrey M.

Karp, Larry S.

Cambridge University Press

Information and Entropy Econometrics - A Review and Synthesis summarizes the basics of information theoretic methods in econometrics and the connecting theme among these methods.  The sub-class of methods that treat the observed sample moments as stochastic is discussed in greater details.  I  Information and Entropy Econometrics - A Review and Synthesis · focuses on inter-connection between information theory, estimation and inference.  · provides a detailed survey of information theoretic concepts and quantities used within econometrics and then show how these quantities are used within IEE.   · pays special attention for the interpretation of these quantities and for describing the relationships between information theoretic estimators and traditional estimators. Readers need a basic knowledge of econometrics, but do not need prior knowledge of information theory.  The survey is self contained and interested readers can replicate all results and examples provided.  Whenever necessary the readers are referred to the relevant literature.   Information and Entropy Econometrics - A Review and Synthesis will benefit researchers looking for a concise introduction to the basics of IEE and to acquire the basic tools necessary for using and understanding these methods. Applied researchers can use the book to learn improved new methods, and applications for extracting information from noisy and limited data and for learning from these data.

Information and Entropy Econometrics: A Review and Synthesis

Now Pub

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