A. Colin Cameron, Pravin K. Trivedi

Regression Analysis of Count Data

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Econometric Society Monographs 53
• Verlag: Cambridge University Press
• 2. Aufl.
• Seitenzahl: 596
• Erscheinungstermin: 15. Juni 2015
• Englisch
• Abmessung: 229mm x 152mm x 32mm
• Gewicht: 810g
• ISBN-13: 9781107667273
• ISBN-10: 1107667275
• Artikelnr.: 37295892

Autorenporträt

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).

Inhaltsangabe

1. Introduction; 2. Model specification and estimation; 3. Basic count regression; 4. Generalized count regression; 5. Model evaluation and testing; 6. Empirical illustrations; 7. Time series data; 8. Multivariate data; 9. Longitudinal data; 10. Endogenous regressors and selection; 11. Flexible methods for counts; 12. Bayesian methods for counts; 13. Measurement errors.

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1. Introduction
2. Model specification and estimation
3. Basic count regression
4. Generalized count regression
5. Model evaluation and testing
6. Empirical illustrations
7. Time series data
8. Multivariate data
9. Longitudinal data
10. Endogenous regressors and selection
11. Flexible methods for counts
12. Bayesian methods for counts
13. Measurement errors.