- Series: Econometric Society Monographs (Book 53)
- Paperback: 596 pages
- Publisher: Cambridge University Press; 2 edition (May 27, 2013)
- Language: English
- ISBN-10: 1107667275
- ISBN-13: 978-1107667273
- Product Dimensions: 6 x 1.3 x 9 inches
- Shipping Weight: 2.2 pounds (View shipping rates and policies)
- Average Customer Review: 6 customer reviews
- Amazon Best Sellers Rank: #547,083 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Regression Analysis of Count Data (Econometric Society Monographs) 2nd Edition
Use the Amazon App to scan ISBNs and compare prices.
"Neverworld Wake" by Marisha Pessl
Read the absorbing new psychological suspense thriller from acclaimed New York Times bestselling author Marisha Pessl. Learn more
Frequently bought together
Customers who viewed this item also viewed
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.
About the Author
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).
Top customer reviews
There was a problem filtering reviews right now. Please try again later.
Overall, I like the book, but from my judge, the authors fail to lead the learner very well into the use and then the connection with the formulas, assumptions, derivations and so on. I prefer the style of writers like Manly or Hilbe who show first what the methods are for in a very easy to grasp way then they proceed with an example.
If I'm asked whether or not to recommend this book, yes, I would recommend it.
I have learned more about regression of count data by reading papers and then coming to understand several of the points Cameron and Trivedi attempted to get across.
The authors intent is for this book to be read by researchers, graduate students and practitioners in the many fields that make use of count data. Chapter 1 introduces count data, the Poisson distribution and the Poisson process and also shows how the Poisson process can be derived based on the assumption of independent and identically distributed exponential waiting times. It concludes with specification of regression models for counts and a number of practical examples where modeling count data would naturally arise. The importance of the integers is emphasized with the quote from Kronecker at the beginning of the chapter, "God made the integers, all the rest is the work of man."
Chapter 2 provides an extensive treatment of model specification and estimation methods. The authors cover many approaches and provide excellent references to the literature. Generalized linear models provide one common approach in the statistics literature and these methods are well described in this chapter.
Poisson regression is the main topic of Chapter 3 but the chapter goes on to discuss negative binomial models that handle overdispersion. An example of data on doctor's visits is used to illustrate the techniques. Statistical tests for overdispersion are also presented. A variety of other modeling techniques are also provided.
More general models including mixture models are considered in Chapter 4. Chapter 5 looks at ways of evaluating potential models. Chapter 6 provides some real economic data from health services to illustrate the methods of the earlier chapters.
Chapter 7 covers time series analysis for integer data. The authors provide information and literature that is not standard in a text on time series analysis but is applicable to count data.
Subsequent chapters deal with more complexity including multivariate data,longitudinal data analysis and measurement error models. Important recent developments in bootstrap methods and Bayesian statistics are covered in the context of problems for which these methods have demonstrated their value.
This is a great reference book for statisticians and econometricians interested in problems involving counting processes. It could also be used for a graduate school text on point process regression.