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Econometrics & Statistical Methods

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This text, intended for both graduate students and professional researchers, is an effective, concise introduction to the structural econometrics of auctions. Tools from recent developments in theoretical econometrics are combined with established numerical methods to provide a practical guide to most of the main concepts in the empirical analysis of field data from auctions. Among other things, the text is remarkable for a large number of mathematical problems and computer exercises for which sample solutions are provided at the end of the book.

With a foreword by Vernon L. Smith, recipient of the 2002 Nobel Prize in Economics.

The study of combinatorial auctions—auctions in which bidders can bid on combinations of items or "packages"—draws on the disciplines of economics, operations research, and computer science. This landmark collection integrates these three perspectives, offering a state-of-the art survey of developments in combinatorial auction theory and practice by leaders in the field.

This book presents a variety of computational methods used to solve dynamic problems in economics and finance. It emphasizes practical numerical methods rather than mathematical proofs and focuses on techniques that apply directly to economic analyses. The examples are drawn from a wide range of subspecialties of economics and finance, with particular emphasis on problems in agricultural and resource economics, macroeconomics, and finance. The book also provides an extensive Web-site library of computer utilities and demonstration programs.The book is divided into two parts.

Quantitative Methods and Applications

This book is an effective, concise text for students and researchers that combines the tools of dynamic programming with numerical techniques and simulation-based econometric methods. Doing so, it bridges the traditional gap between theoretical and empirical research and offers an integrated framework for studying applied problems in macroeconomics and microeconomics.In part I the authors first review the formal theory of dynamic optimization; they then present the numerical tools and econometric techniques necessary to evaluate the theoretical models.

This book provides a comprehensive introduction to the mathematical foundations of economics, from basic set theory to fixed point theorems and constrained optimization. Rather than simply offer a collection of problem-solving techniques, the book emphasizes the unifying mathematical principles that underlie economics. Features include an extended presentation of separation theorems and their applications, an account of constraint qualification in constrained optimization, and an introduction to monotone comparative statics. These topics are developed by way of more than 800 exercises.

Historically, the theory of forecasting that underpinned actual practice in economics has been based on two key assumptions?-that the model was a good representation of the economy and that the structure of the economy would remain relatively unchanged. In reality, forecast models are mis-specified, the economy is subject to unanticipated shifts, and the failure to make accurate predictions is relatively common.

To harness the full power of computer technology, economists need to use a broad range of mathematical techniques. In this book, Kenneth Judd presents techniques from the numerical analysis and applied mathematics literatures and shows how to use them in economic analyses.The book is divided into five parts. Part I provides a general introduction. Part II presents basics from numerical analysis on R^n,including linear equations, iterative methods, optimization, nonlinear equations, approximation methods, numerical integration and differentiation, and Monte Carlo methods.

The Bayesian revolution in statistics—where statistics is integrated with decision making in areas such as management, public policy, engineering, and clinical medicine—is here to stay. Introduction to Statistical Decision Theory states the case and in a self-contained, comprehensive way shows how the approach is operational and relevant for real-world decision making under uncertainty.

A Comparative Approach

The standard introductory texts to mathematical statistics leave the Bayesian approach to be taught later in advanced topics courses—giving students the impression that Bayesian statistics provide but a few techniques appropriate in only special circumstances. Nothing could be further from the truth, argues Dale Poirier, who has developed a course for teaching comparatively both the classical and the Bayesian approaches to econometrics.

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