New Perspectives in Econometric Theory comprises specially selected papers by Halbert White which reflect his research in a variety of related areas in econometrics: heteroskedasticity of unknown form; nonlinear and nonparametric regression; instrumental variables and generalized method of moments estimation; and measurability and limit theory. In many instances, results from one paper provide the foundation for, or suggest new directions for, research taken up by others in the collection. The intent of collecting these papers together in the present volume, with new commentaries by the author, is to provide access both to a modern unified perspective for econometric theory and to a set of concepts and tools that will be useful to practitioners in the field. As a companion to the first volume entitled Advances in Econometric Theory, this latest selection of Halbert White's work will appeal to academics and researchers in econometrics and economic theory.

Halbert White has made a major contribution to key areas of econometrics including specification analysis, specification testing, encompassing and Cox tests and model selection. This book presents his most important published work supplemented with new material setting his work in context.Together with new introductions to each of the chapters, the articles cover work from the early 1980s to 1996 and provide an excellent overview of the breadth of Professor White's work and the evolution of his ideas. Using rigorous mathematical techniques Halbert White develops many of the central themes in econometrics concerning models, data generating processes and estimation procedures. Throughout the book the unifying vision is that econometric models are only imperfect approximations to the processes generating economic data and that this has implications for the interpretation of estimates, inference and selection of econometric models. This unique collection of some of Halbert White's important work, not otherwise readily accessible, will be welcomed by researchers, graduates and academics in econometrics and statistics.

The book is a collection of essays in honour of Clive Granger. The chapters are by some of the world's leading econometricians, all of whom have collaborated with or studied with (or both) Clive Granger. Central themes of Granger's work are reflected in the book with attention to tests for unit roots and cointegration, tests of misspecification, forecasting models and forecast evaluation, non-linear and non-parametric econometric techniques, and overall, a careful blend of practical empirical work and strong theory. The book shows the scope of Granger's research and the range of the profession that has been influenced by his work.

This book provides the tools and concepts necessary to study the behavior of econometric estimators and test statistics in large samples. An econometric estimator is a solution to an optimization problem; that is, a problem that requires a body of techniques to determine a specific solution in a defined set of possible alternatives that best satisfies a selected object function or set of constraints. Thus, this highly mathematical book investigates situations concerning large numbers, in which the assumptions of the classical linear model fail. Economists, of course, face these situations often.
Key Features
* Completely revised Chapter Seven on functional central limit theory and its applications, specifically unit root regression, spurious regression, and regression with cointegrated processes
* Updated material on:
* Central limit theory
* Asymptotically efficient instrumental variables estimation
* Estimation of asymptotic covariance matrices
* Efficient estimation with estimated error covariance matrices
* Efficient IV estimation

This book brings together presentations of some of the fundamental new research that has begun to appear in the areas of dynamic structural modeling, nonlinear structural modeling, time series modeling, nonparametric inference, and chaotic attractor inference. The contents of this volume comprise the proceedings of the third of a conference series entitled International Symposia in Economic Theory and Econometrics. This conference was held at the IC;s2 (Innovation, Creativity and Capital) Institute at the University of Texas at Austin on May 22-23, l986.

This book brings together presentations of some of the fundamental new research that has begun to appear in the areas of dynamic structural modeling, nonlinear structural modeling, time series modeling, nonparametric inference, and chaotic attractor inference. The contents of this volume comprise the proceedings of the third of a conference series entitled International Symposia in Economic Theory and Econometrics. This conference was held at the IC;s2 (Innovation, Creativity and Capital) Institute at the University of Texas at Austin on May 22-23, l986.

This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models, and gives the conditions under which parameters of interest can be consistently estimated despite misspecification, and the consequences of misspecification, for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. Although the theory presented in the book is motivated by econometric problems, its applicability is by no means restricted to economics. Subject to defined limitations, the theory applies to any scientific context in which statistical analysis is conducted using approximate models.

This book examines the consequences of misspecifications ranging from the fundamental to the nonexistent for the interpretation of likelihood-based methods of statistical estimation and interference. Professor White first explores the underlying motivation for maximum-likelihood estimation, treats the interpretation of the maximum-likelihood estimator (MLE) for misspecified probability models, and gives the conditions under which parameters of interest can be consistently estimated despite misspecification, and the consequences of misspecification, for hypothesis testing in estimating the asymptotic covariance matrix of the parameters. Although the theory presented in the book is motivated by econometric problems, its applicability is by no means restricted to economics. Subject to defined limitations, the theory applies to any scientific context in which statistical analysis is conducted using approximate models.