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The article provides an overview of research papers and topics in this issue of "Econometric Reviews" including tests for nonstationarity using maximum entropy resampling, information theory, and entropy econometrics.

Chapter 8 of the book "Foundations and Trends in Econometrics" is presented. It provides an overview of information theory (IT)-related topics that fall within Information and Entropy Econometrics (IEE,) and which are not discussed in the book. These include literature on the information theory-generalized method of moments (IT-GMM), the minimum description length (MDL), and the Bayesian information theory models.

Chapter 7 of the book "Foundations and Trends in Econometrics" is presented. It determines the interconnection between information theory (IT), likelihood and inference. It examines the problem of Matrix Balancing with the zero-moment conditions and stochastic moment conditions. It also presents an example of a Matrix Balancing problem.

Chapter 4 of the book "Foundations and Trends in Econometrics" is presented. It focuses on the Maximum Entropy (ME) principle and provides a summary of the principle. It discusses the entropy concentration theorem (ECT) and how it relates to the formulation of the ME principle, and presents the die example that shows the ME solution.

Chapter 5 of the book "Foundations and Trends in Econometrics" is presented. It discusses the information-theoretic (IT) methods of estimation, including the Empirical Likelihood (EL), the Generalized Maximum Entropy (GME), and the Generalized Method of Moments (GMM). It explores the connection between these methods and information theory.

Chapter 6 of the book "Foundations and Trends in Econometrics" is presented. It focuses on the information-theoretic (IT) methods of estimation with stochastic moment conditions. It discusses the generic IT estimator and the two basic members of IT classification, where the moment conditions are specified as zero and the conditions are stochastic.

Chapter 3 of the book "Foundations and Trends in Econometrics" is presented. It provides a summary on the basics of information theory (IT). It discusses the meaning of Information and Entropy Econometrics (IEE) and the concepts that relate to IEE. It also presents the mathematical formulas for obtaining the information entropy.

Chapter 2 of the book "Foundations and Trends in Econometrics" is presented. It focuses on the history of statistical and econometrics thoughts, which led to the current state of Information and Entropy Econometrics (IEE). It discusses the studies that relate to IEE and show the historical perspectives of IEE.

Chapter 1 of the book "Foundations and Trends in Econometrics" is presented. It gives an overview of a survey that examines the concept of information as it relates to econometric and statistical analyses of data. It discusses the goals of the survey, which are to study the basics of information-theoretic (IT) methods in econometrics, to examine the connection of these methods, and to provide a detailed summary of the sub-class IT estimators that view the observed moments as stochastic.

A semiparametric estimator for evaluating the parameters of data generated under a sample selection process is developed. This estimator is based on the generalized maximum entropy estimator and performs well for small and ill-posed samples. Theoretical and sampling comparisons with parametric and semiparametric estimators are given. This method and standard ones are applied to three small-sample empirical applications of the wage-participation model for female teenage heads of households, immigrants, and Native Americans. [ABSTRACT FROM AUTHOR]

Abstract--We use a nonlinear, nonparametric method to forecast unemployment rates. This method is an extension of the nearest-neighbor method but uses a higher-dimensional simplex approach. We compare these forecasts with several linear and nonlinear parametric methods based on the work of Montgomery et al. (1998) and Carruth et al. (1998). Our main result is that, due to the nonlinearity in the data-generating process, the nonparametric method outperforms many other well-known models, even when these models use more information. This result holds for forecasts based on quarterly and on monthly data. [ABSTRACT FROM AUTHOR]

The overall objectives of this review and synthesis are to study the basics of information-theoretic methods in econometrics, to examine the connecting theme among these methods, and to provide a more detailed summary and synthesis of the sub-class of methods that treat the observed sample moments as stochastic. Within the above objectives, this review focuses on studying the inter-connection between information theory, estimation, and inference. To achieve these objectives, it provides a detailed survey of information-theoretic concepts and quantities used within econometrics. It also illustrates the use of these concepts and quantities within the subfield of information and entropy econometrics while paying special attention to the interpretation of these quantities. The relationships between information-theoretic estimators and traditional estimators are discussed throughout the survey. This synthesis shows that in many cases information-theoretic concepts can be incorporated within the traditional likelihood approach and provide additional insights into the data processing and the resulting inference. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss various reports within the issue about econometrics and the contributions of American Arnold Zellner to science and econometrics and information theoretical thinking.