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 provides the most comprehensive and up-to-date account of models and methods to interpret such data. The authors have conducted research in the field for more than twenty-five years. In this book, they 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 book may be used as a reference work on count models or by students seeking an authoritative overview. Complementary material in the form of data sets, template programs, and bibliographic resources can be accessed on the Internet through the authors' homepages. This second edition is an expanded and updated version of the first, with new empirical examples and more than one hundred new references added. 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.
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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 book may be used as a reference work on count models or by students seeking an authoritative overview.
A. Colin Cameron is Professor of Economics at the University of California, Davis. His research and teaching interests span a range of topics in microeconometrics. He is a past director of the Center on Quantitative Social Science at the University of California, Davis and is currently an associate editor of the Stata Journal. He is coauthor (with Pravin K. Trivedi) of the first edition of Regression Analysis of Count Data (Cambridge, 1998) and of Microeconometrics: Methods and Applications (Cambridge, 2005).
Pravin K. Trivedi is Distinguished Professor and J. H. Rudy Professor of Economics at Indiana University, Bloomington. His research and teaching interests are in microeconometrics and health economics. He served as co-editor of the Econometrics Journal from 2000 to 2007 and has been on the board of Journal of Applied Econometrics since 1988. He is coauthor (with A. Colin Cameron) of the first edition of Regression Analysis of Count Data (Cambridge, 1998) and of Microeconometrics: Methods and Applications (Cambridge, 2005).
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