Econometric Analysis Of Count Data
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| Author | : Rainer Winkelmann |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 291 |
| Release | : 2013-06-29 |
| Genre | : Business & Economics |
| ISBN | : 3662041499 |
The primary objective of this book is to provide an introduction to the econometric modeling of count data for graduate students and researchers. It should serve anyone whose interest lies either in developing the field fur ther, or in applying existing methods to empirical questions. Much of the material included in this book is not specific to economics, or to quantita tive social sciences more generally, but rather extends to disciplines such as biometrics and technometrics. Applications are as diverse as the number of congressional budget vetoes, the number of children in a household, and the number of mechanical defects in a production line. The unifying theme is a focus on regression models in which a dependent count variable is modeled as a function of independent variables which mayor may not be counts as well. The modeling of count data has come of age. Inclusion of some of the fundamental models in basic textbooks, and implementation on standard computer software programs bear witness to that. Based on the standard Poisson regression model, numerous extensions and alternatives have been developed to address the common challenges faced in empirical modeling (unobserved heterogeneity, selectivity, endogeneity, measurement error, and dependent observations in the context of panel data or multivariate data, to name but a few) as well as the challenges that are specific to count data (e. g. , over dispersion and underdispersion).
| Author | : Rainer Winkelmann |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 316 |
| Release | : 2013-11-11 |
| Genre | : Business & Economics |
| ISBN | : 3540247289 |
Graduate students and researchers are provided with an up-to-date survey of statistical and econometric techniques for the analysis of count data, with a focus on conditional distribution models. Proper count data probability models allow for rich inferences, both with respect to the stochastic count process that generated the data, and with respect to predicting the distribution of outcomes. The book starts with a presentation of the benchmark Poisson regression model. Alternative models address unobserved heterogeneity, state dependence, selectivity, endogeneity, underreporting, and clustered sampling. Testing and estimation is discussed from frequentist and Bayesian perspectives. Finally, applications are reviewed in fields such as economics, marketing, sociology, demography, and health sciences. The fourth edition contains several new sections, for example on nonnested hurdle models, quantile regression and on software. Many other sections have been entirely rewritten and extended.
| Author | : Adrian Colin Cameron |
| Publisher | : Cambridge University Press |
| Total Pages | : 597 |
| Release | : 2013-05-27 |
| Genre | : Business & Economics |
| ISBN | : 1107014166 |
This book provides the most comprehensive and up-to-date account of regression methods to explain the frequency of events.
| Author | : A. Colin Cameron |
| Publisher | : Cambridge University Press |
| Total Pages | : 436 |
| Release | : 1998-09-28 |
| Genre | : Business & Economics |
| ISBN | : 9780521635677 |
This analysis provides a comprehensive account of models and methods to interpret frequency data.
| Author | : Jeffrey M. Wooldridge |
| Publisher | : MIT Press |
| Total Pages | : 1095 |
| Release | : 2010-10-01 |
| Genre | : Business & Economics |
| ISBN | : 0262232588 |
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.
| Author | : Andrew M. Jones |
| Publisher | : John Wiley & Sons |
| Total Pages | : 252 |
| Release | : 2002-05-17 |
| Genre | : Medical |
| ISBN | : 9780470841457 |
Given extensive use of individual level data in Health Economics, it has become increasingly important to understand the microeconometric techniques available to applied researchers. The purpose of this book is to give readers convenient access to a collection of recent contributions that contain innovative applications of microeconometric methods to data on health and health care. Contributions are selected from papers presented at the European Workshops on Econometrics and Health Economics and published in Health Economics. Topics covered include: * Latent Variables * Unobservable heterogeneity and selection problems * Count data and survival analysis * Flexible and semiparametric estimators for limited dependent variables * Classical and simulation methods for panel data * Publication marks the tenth anniversary of the Workshop series. Doctoral students and researchers in health economics and microeconomics will find this book invaluable. Researchers in related fields such as labour economics and biostatistics will also find the content of use.
| Author | : A. Colin Cameron |
| Publisher | : Cambridge University Press |
| Total Pages | : 597 |
| Release | : 2013-05-27 |
| Genre | : Business & Economics |
| ISBN | : 1107717795 |
Students in both social and natural sciences often seek regression methods to explain the frequency of events, such as visits to a doctor, auto accidents, or new patents awarded. This book, now in its second edition, provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors combine theory and practice to make sophisticated methods of analysis accessible to researchers and practitioners working with widely different types of data and software in areas such as applied statistics, econometrics, marketing, operations research, actuarial studies, demography, biostatistics and quantitative social sciences. The new material includes new theoretical topics, an updated and expanded treatment of cross-section models, coverage of bootstrap-based and simulation-based inference, expanded treatment of time series, multivariate and panel data, expanded treatment of endogenous regressors, coverage of quantile count regression, and a new chapter on Bayesian methods.
| Author | : Simon P. Washington |
| Publisher | : CRC Press |
| Total Pages | : 546 |
| Release | : 2010-12-02 |
| Genre | : Technology & Engineering |
| ISBN | : 142008285X |
The complexity, diversity, and random nature of transportation problems necessitates a broad analytical toolbox. Describing tools commonly used in the field, Statistical and Econometric Methods for Transportation Data Analysis, Second Edition provides an understanding of a broad range of analytical tools required to solve transportation problems. It includes a wide breadth of examples and case studies covering applications in various aspects of transportation planning, engineering, safety, and economics. After a solid refresher on statistical fundamentals, the book focuses on continuous dependent variable models and count and discrete dependent variable models. Along with an entirely new section on other statistical methods, this edition offers a wealth of new material. New to the Second Edition A subsection on Tobit and censored regressions An explicit treatment of frequency domain time series analysis, including Fourier and wavelets analysis methods New chapter that presents logistic regression commonly used to model binary outcomes New chapter on ordered probability models New chapters on random-parameter models and Bayesian statistical modeling New examples and data sets Each chapter clearly presents fundamental concepts and principles and includes numerous references for those seeking additional technical details and applications. To reinforce a practical understanding of the modeling techniques, the data sets used in the text are offered on the book’s CRC Press web page. PowerPoint and Word presentations for each chapter are also available for download.
| Author | : Bryan Graham |
| Publisher | : Academic Press |
| Total Pages | : 244 |
| Release | : 2020-06-03 |
| Genre | : Business & Economics |
| ISBN | : 0128117710 |
The Econometric Analysis of Network Data serves as an entry point for advanced students, researchers, and data scientists seeking to perform effective analyses of networks, especially inference problems. It introduces the key results and ideas in an accessible, yet rigorous way. While a multi-contributor reference, the work is tightly focused and disciplined, providing latitude for varied specialties in one authorial voice. Answers both 'why' and 'how' questions in network analysis, bridging the gap between practice and theory allowing for the easier entry of novices into complex technical literature and computation Fully describes multiple worked examples from the literature and beyond, allowing empirical researchers and data scientists to quickly access the 'state of the art' versioned for their domain environment, saving them time and money Disciplined structure provides latitude for multiple sources of expertise while retaining an integrated and pedagogically focused authorial voice, ensuring smooth transition and easy progression for readers Fully supported by companion site code repository 40+ diagrams of 'networks in the wild' help visually summarize key points
| Author | : Rainer Winkelmann |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 223 |
| Release | : 2013-11-11 |
| Genre | : Business & Economics |
| ISBN | : 366221735X |
This book presents statistical methods for the analysis of events. The primary focus is on single equation cross section models. The book addresses both the methodology and the practice of the subject and it provides both a synthesis of a diverse body of literature that hitherto was available largely in pieces, as well as a contribution to the progress of the methodology, establishing several new results and introducing new models. Starting from the standard Poisson regression model as a benchmark, the causes, symptoms and consequences of misspecification are worked out. Both parametric and semi-parametric alternatives are discussed. While semi-parametric models allow for robust interference, parametric models can identify features of the underlying data generation process.
