Econometric Foundations establishes a new paradigm for teaching econometric problems to talented upper-level undergraduates, graduate students, and professionals. The complete package (text, accompanying CD-ROM, and electronic guide) provides relevance, clarity, and organization to those wishing to acquaint themselves with the principles and procedures for information processing and recovery from samples of economic data. In the real world such data are usually limited or incomplete, and the parameters sought are unobserved and not subject to direct observation or measurement. Econometric Foundations fully provides an operational understanding of a rich set of estimation and inference tools to master such data, including traditional likelihood based and non-traditional non-likelihood based procedures, that can be used in conjunction with the computer to address economic problems. The accompanying CD-ROM contains reviews of probability theory, principles of classical estimation and inference, and handling of ill-posed inverse problems in text-searchable electronic documents, an interactive Matrix Review manual with GAUSS LIGHT software, and an electronic Examples Manual. A separate Guide, which may be accessed through the Internet, further enhances the student's mastery of the topics by providing solutions guides to the questions and problems in the text. This text, CD-ROM, and electronic guide package make Econometric Foundations the most up-to-date and comprehensive learning resource available.

Econometric Foundations Pack with CD-ROM

Business & Money

Mittelhammer, Ron C.

Judge, George G.

Miller, Douglas J.

Economics

Cambridge University Press

An Information Theoretic Approach to Econometrics

This book is intended to provide the reader with a firm conceptual and empirical understanding of basic information-theoretic econometric models and methods. Because most data are observational, practitioners work with indirect noisy observations and ill-posed econometric models in the form of stochastic inverse problems. Consequently, traditional econometric methods in many cases are not applicable for answering many of the quantitative questions that analysts wish to ask. After initial chapters deal with parametric and semiparametric linear probability models, the focus turns to solving nonparametric stochastic inverse problems. In succeeding chapters, a family of power divergence measure-likelihood functions are introduced for a range of traditional and nontraditional econometric-model problems. Finally, within either an empirical maximum likelihood or loss context, Ron C. Mittelhammer and George G. Judge suggest a basis for choosing a member of the divergence family.

Mathematical Statistics for Economics and Business

Mathematical Statistics for Economics and Business, Second Edition, provides a comprehensive introduction to the principles of mathematical statistics which underpin statistical analyses in the fields of economics, business, and econometrics. The selection of topics in this textbook is designed to provide students with a conceptual foundation that will facilitate a substantial understanding of statistical applications in these subjects. This new edition has been updated throughout and now also includes a downloadable Student Answer Manual containing detailed solutions to half of the over 300 end-of-chapter problems. 
After introducing the concepts of probability, random variables, and probability density functions, the author develops the key concepts of mathematical statistics, most notably: expectation, sampling, asymptotics, and the main families of distributions. The latter half of the book is then devoted to the theories of estimation and hypothesis testing with associated examples and problems that indicate their wide applicability in economics and business. Features of the new edition include: a reorganization of topic flow and presentation to facilitate reading and understanding; inclusion of additional topics of relevance to statistics and econometric applications; a more streamlined and simple-to-understand notation for multiple integration and multiple summation over general sets or vector arguments; updated examples; new end-of-chapter problems; a solution manual for students; a comprehensive answer manual for instructors; and a theorem and definition map.
This book has evolved from numerous graduate courses in mathematical statistics and econometrics taught by the author, and will be ideal for students beginning graduate study as well as for advanced undergraduates.

Springer

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Books by Mittelhammer, Ron C.