Identification and Inference for Econometric Models

Identification and Inference for Econometric Models PDF Author: Donald W. K. Andrews
Publisher: Cambridge University Press
ISBN: 1139444603
Category : Business & Economics
Languages : en
Pages : 589

Get Book Here

Book Description
This 2005 volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose others. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.

Identification and Inference for Econometric Models

Identification and Inference for Econometric Models PDF Author: Donald W. K. Andrews
Publisher: Cambridge University Press
ISBN: 1139444603
Category : Business & Economics
Languages : en
Pages : 589

Get Book Here

Book Description
This 2005 volume contains the papers presented in honor of the lifelong achievements of Thomas J. Rothenberg on the occasion of his retirement. The authors of the chapters include many of the leading econometricians of our day, and the chapters address topics of current research significance in econometric theory. The chapters cover four themes: identification and efficient estimation in econometrics, asymptotic approximations to the distributions of econometric estimators and tests, inference involving potentially nonstationary time series, such as processes that might have a unit autoregressive root, and nonparametric and semiparametric inference. Several of the chapters provide overviews and treatments of basic conceptual issues, while others advance our understanding of the properties of existing econometric procedures and/or propose others. Specific topics include identification in nonlinear models, inference with weak instruments, tests for nonstationary in time series and panel data, generalized empirical likelihood estimation, and the bootstrap.

Exploration Of A Nonlinear World: An Appreciation Of Howell Tong's Contributions To Statistics

Exploration Of A Nonlinear World: An Appreciation Of Howell Tong's Contributions To Statistics PDF Author: Kung-sik Chan
Publisher: World Scientific
ISBN: 9814469440
Category : Mathematics
Languages : en
Pages : 412

Get Book Here

Book Description
This festschrift is dedicated to Professor Howell Tong on the occasion of his 65th birthday. With a Foreword written by Professor Peter Whittle, FRS, it celebrates Tong's path-breaking and tireless contributions to nonlinear time series analysis, chaos and statistics, by reprinting 10 selected papers by him and his collaborators, which are interleaved with 17 original reviews, written by 19 international experts.Through these papers and reviews, readers will have an opportunity to share many of the excitements, retrospectively and prospectively, of the relatively new subject of nonlinear time series. Tong has played a leading role in laying the foundation of the subject; his innovative and authoritative contributions are reflected in the review articles in the volume, which describe modern and related developments in the subject, including applications in many major fields such as ecology, economics, finance and others. This volume will be useful to researchers and students interested in the theory and practice of nonlinear time series analysis.

The Work of Raymond J. Carroll

The Work of Raymond J. Carroll PDF Author: Marie Davidian
Publisher: Springer
ISBN: 3319058010
Category : Mathematics
Languages : en
Pages : 599

Get Book Here

Book Description
This volume contains Raymond J. Carroll's research and commentary on its impact by leading statisticians. Each of the seven main parts focuses on a key research area: Measurement Error, Transformation and Weighting, Epidemiology, Nonparametric and Semiparametric Regression for Independent Data, Nonparametric and Semiparametric Regression for Dependent Data, Robustness, and other work. The seven subject areas reviewed in this book were chosen by Ray himself, as were the articles representing each area. The commentaries not only review Ray’s work, but are also filled with history and anecdotes. Raymond J. Carroll’s impact on statistics and numerous other fields of science is far-reaching. His vast catalog of work spans from fundamental contributions to statistical theory to innovative methodological development and new insights in disciplinary science. From the outset of his career, rather than taking the “safe” route of pursuing incremental advances, Ray has focused on tackling the most important challenges. In doing so, it is fair to say that he has defined a host of statistics areas, including weighting and transformation in regression, measurement error modeling, quantitative methods for nutritional epidemiology and non- and semiparametric regression.

Partially Linear Models

Partially Linear Models PDF Author: Wolfgang Härdle
Publisher: Springer Science & Business Media
ISBN: 9783790813005
Category : Business & Economics
Languages : en
Pages : 218

Get Book Here

Book Description
In the last ten years, there has been increasing interest and activity in the general area of partially linear regression smoothing in statistics. Many methods and techniques have been proposed and studied. This monograph hopes to bring an up-to-date presentation of the state of the art of partially linear regression techniques. The emphasis is on methodologies rather than on the theory, with a particular focus on applications of partially linear regression techniques to various statistical problems. These problems include least squares regression, asymptotically efficient estimation, bootstrap resampling, censored data analysis, linear measurement error models, nonlinear measurement models, nonlinear and nonparametric time series models.

Journal of the Japanese Society of Computational Statistics

Journal of the Japanese Society of Computational Statistics PDF Author:
Publisher:
ISBN:
Category : Mathematical statistics
Languages : en
Pages : 500

Get Book Here

Book Description


Cowles Foundation Discussion Paper

Cowles Foundation Discussion Paper PDF Author: Yale University. Cowles Foundation for Research in Economics
Publisher:
ISBN:
Category : Economics
Languages : en
Pages : 636

Get Book Here

Book Description


Scientific and Technical Aerospace Reports

Scientific and Technical Aerospace Reports PDF Author:
Publisher:
ISBN:
Category : Aeronautics
Languages : en
Pages : 984

Get Book Here

Book Description


Journal of Econometrics

Journal of Econometrics PDF Author:
Publisher:
ISBN:
Category : Econometrics
Languages : en
Pages : 860

Get Book Here

Book Description


Mathematical Reviews

Mathematical Reviews PDF Author:
Publisher:
ISBN:
Category : Mathematics
Languages : en
Pages : 1852

Get Book Here

Book Description


Longitudinal Structural Equation Modeling

Longitudinal Structural Equation Modeling PDF Author: Jason T. Newsom
Publisher: Routledge
ISBN: 1317975359
Category : Psychology
Languages : en
Pages : 437

Get Book Here

Book Description
This comprehensive resource reviews structural equation modeling (SEM) strategies for longitudinal data to help readers see which modeling options are available for which hypotheses. The author demonstrates how SEM is related to other longitudinal data techniques throughout. By exploring connections between models, readers gain a better understanding of when to choose one analysis over another. The book explores basic models to sophisticated ones including the statistical and conceptual underpinnings that are the building blocks of the analyses. Accessibly written, research examples from the behavioral and social sciences and results interpretations are provided throughout. The emphasis is on concepts and practical guidance for applied research rather than on mathematical proofs. New terms are highlighted and defined in the glossary. Figures are included for every model along with detailed discussions of model specification and implementation issues. Each chapter also includes examples of each model type, comment sections that provide practical guidance, model extensions, and recommended readings. Highlights include: Covers the major SEM approaches to longitudinal analysis in one resource. Explores connections between longitudinal SEM models to enhance integration. Numerous examples that help readers match research questions to appropriate analyses and interpret results. Reviews practical issues related to model specification and estimation to reinforce connections. Analyzes continuous and discrete (binary and ordinal) variables throughout for breadth not found in other sources. Reviews key SEM concepts for those who need a refresher (Ch. 1). Emphasizes how to apply and interpret each model through realistic data examples. Provides the book’s data sets at www.longitudinalsem.com along with the Mplus and R-lavaan syntax used to generate the results. Introduces the LISREL notation system used throughout (Appendix A). The chapters can be read out of order but it is best to read chapters 1 – 4 first because most of the later chapters refer back to them. The book opens with a review of latent variables and analysis of binary and ordinal variables. Chapter 2 applies this information to assessing longitudinal measurement invariance. SEM tests of dependent means and proportions over time points are explored in Chapter 3, and stability and change, difference scores, and lagged regression are covered in Chapter 4. The remaining chapters are each devoted to one major type of longitudinal SEM -- repeated measures analysis models, full cross-lagged panel models and simplex models, modeling stability with state-trait models, linear and nonlinear growth curve models, latent difference score models, latent transition analysis, time series analysis, survival analysis, and attrition. Missing data is discussed in the context of many of the preceding models in Chapter 13. Ideal for graduate courses on longitudinal (data) analysis, advanced SEM, longitudinal SEM, and/or advanced data (quantitative) analysis taught in the behavioral, social, and health sciences, this text also appeals to researchers in these fields. Intended for those without an extensive math background, prerequisites include familiarity with basic SEM. Matrix algebra is avoided in all but a few places.