Panel Data Econometrics with R

Panel Data Econometrics with R

Author: Yves Croissant

Publisher: John Wiley & Sons

Published: 2018-08-10

Total Pages: 435

ISBN-13: 1118949188

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Panel Data Econometrics with R provides a tutorial for using R in the field of panel data econometrics. Illustrated throughout with examples in econometrics, political science, agriculture and epidemiology, this book presents classic methodology and applications as well as more advanced topics and recent developments in this field including error component models, spatial panels and dynamic models. They have developed the software programming in R and host replicable material on the book’s accompanying website.


Panel Data Econometrics

Panel Data Econometrics

Author: Manuel Arellano

Publisher: Oxford University Press

Published: 2003

Total Pages: 244

ISBN-13: 0199245282

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Written by one of the world's leading experts on dynamic panel data reviews, this volume reviews most of the important topics in the subject. It deals with static models, dynamic models, discrete choice and related models.


Latent Curve Models

Latent Curve Models

Author: Kenneth A. Bollen

Publisher: John Wiley & Sons

Published: 2005-12-23

Total Pages: 312

ISBN-13: 047145592X

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An effective technique for data analysis in the social sciences The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters governing these trajectories. The authors synthesize a vast amount of research and findings and, at the same time, provide original results. The book analyzes LCMs from the perspective of structural equation models (SEMs) with latent variables. While the authors discuss simple regression-based procedures that are useful in the early stages of LCMs, most of the presentation uses SEMs as a driving tool. This cutting-edge work includes some of the authors' recent work on the autoregressive latent trajectory model, suggests new models for method factors in multiple indicators, discusses repeated latent variable models, and establishes the identification of a variety of LCMs. This text has been thoroughly class-tested and makes extensive use of pedagogical tools to aid readers in mastering and applying LCMs quickly and easily to their own data sets. Key features include: Chapter introductions and summaries that provide a quick overview of highlights Empirical examples provided throughout that allow readers to test their newly found knowledge and discover practical applications Conclusions at the end of each chapter that stress the essential points that readers need to understand for advancement to more sophisticated topics Extensive footnoting that points the way to the primary literature for more information on particular topics With its emphasis on modeling and the use of numerous examples, this is an excellent book for graduate courses in latent trajectory models as well as a supplemental text for courses in structural modeling. This book is an excellent aid and reference for researchers in quantitative social and behavioral sciences who need to analyze longitudinal data.


Longitudinal and Panel Data

Longitudinal and Panel Data

Author: Edward W. Frees

Publisher: Cambridge University Press

Published: 2004-08-16

Total Pages: 492

ISBN-13: 9780521535380

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An introduction to foundations and applications for quantitatively oriented graduate social-science students and individual researchers.


Analog Estimation Methods in Econometrics

Analog Estimation Methods in Econometrics

Author: Charles F. Manski

Publisher: Chapman and Hall/CRC

Published: 1988-06-15

Total Pages: 186

ISBN-13:

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Presents familiar elements of estimation theory from an analog perspective discussing recent developments in the theory of analog estimation and new results that offer flexibility in empirical research. Annotation copyrighted by Book News, Inc., Portland, OR


Introduction to Spatial Econometrics

Introduction to Spatial Econometrics

Author: James LeSage

Publisher: CRC Press

Published: 2009-01-20

Total Pages: 362

ISBN-13: 1420064258

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Although interest in spatial regression models has surged in recent years, a comprehensive, up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial Econometrics presents a variety of regression methods used to analyze spatial data samples that violate the traditional assumption of independence between observat


Econometric Analysis of Cross Section and Panel Data, second edition

Econometric Analysis of Cross Section and Panel Data, second edition

Author: Jeffrey M. Wooldridge

Publisher: MIT Press

Published: 2010-10-01

Total Pages: 1095

ISBN-13: 0262296799

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The second edition of a comprehensive state-of-the-art graduate level text on microeconometric methods, substantially revised and updated. The second edition of this acclaimed graduate text provides a unified treatment of two methods used in contemporary econometric research, cross section and data panel methods. By focusing on assumptions that can be given behavioral content, the book maintains an appropriate level of rigor while emphasizing intuitive thinking. The analysis covers both linear and nonlinear models, including models with dynamics and/or individual heterogeneity. In addition to general estimation frameworks (particular methods of moments and maximum likelihood), specific linear and nonlinear methods are covered in detail, including probit and logit models and their multivariate, Tobit models, models for count data, censored and missing data schemes, causal (or treatment) effects, and duration analysis. Econometric Analysis of Cross Section and Panel Data was the first graduate econometrics text to focus on microeconomic data structures, allowing assumptions to be separated into population and sampling assumptions. This second edition has been substantially updated and revised. Improvements include a broader class of models for missing data problems; more detailed treatment of cluster problems, an important topic for empirical researchers; expanded discussion of "generalized instrumental variables" (GIV) estimation; new coverage (based on the author's own recent research) of inverse probability weighting; a more complete framework for estimating treatment effects with panel data, and a firmly established link between econometric approaches to nonlinear panel data and the "generalized estimating equation" literature popular in statistics and other fields. New attention is given to explaining when particular econometric methods can be applied; the goal is not only to tell readers what does work, but why certain "obvious" procedures do not. The numerous included exercises, both theoretical and computer-based, allow the reader to extend methods covered in the text and discover new insights.


Structural Analysis of Discrete Data with Econometric Applications

Structural Analysis of Discrete Data with Econometric Applications

Author: Charles F. Manski

Publisher: MIT Press (MA)

Published: 1981

Total Pages: 512

ISBN-13:

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The thirteen papers in "Structural Analysis of Discrete Data" are previously unpublished major research contributions solicited by the editors. They have been specifically prepared to fulfill the two-fold purpose of the volume, first to provide the econometrics student with an overview of the present extent of the subject and to delineate the boundaries of current research, both in terms of methodology and applications. "Coordinated publication of important findings" should, as the editors state, "lower the cost of entry into the field and speed dissemination of recent research into the graduate econometrics classroom."A second purpose of the volume is to communicate results largely reported in the econometrics literature to a wider community of researchers to whom they are directly relevant, including applied econometricians, statisticians in the area of discrete multivariate analysis, specialists in biometrics, psychometrics, and sociometrics, and analysts in various applied fields such as finance, marketing, and transportation.The papers are grouped into four sections: "Statistical Analysis of Discrete Probability Models, " with papers by the editors and by Steven Cosslett; "Dynamic Discrete Probability Models, " consisting of two contributions by James Heckman; "Structural Discrete Probability Models Derived from Theories of Choice, " with papers by Daniel McFadden, Gregory Fischer and Daniel Nagin, Steven Lerman and Charles Manski, and Moshe Ben-Akiva and Thawat Watanatada; and "Simultaneous Systems Models with Discrete Endogenous Variables, " with contributions by Lung-Fei Lee, Jerry Hausman and David Wise, Dale Poirier, Peter Schmidt, and Robert Avery.Among the applications treated are income maintenance experiments, physician behavior, consumer credit, and intra-urban location and transportation.