Econometric analysis of count data

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This book aims to introduce econometric modeling of count data for graduate students and researchers, catering to those interested in advancing the field or applying existing methods to empirical questions. The content is relevant not only to economics but also to various disciplines, including biometrics and technometrics. Applications range widely, from congressional budget vetoes to household sizes and mechanical defects in production lines. The central theme focuses on regression models where a dependent count variable is explained by independent variables, which may also be counts. The field of count data modeling has matured, evidenced by its inclusion in basic textbooks and implementation in standard software. Building on the Poisson regression model, numerous extensions and alternatives have been created to tackle common empirical challenges such as unobserved heterogeneity, selectivity, endogeneity, measurement error, and dependent observations in panel or multivariate data. Additionally, the book addresses specific challenges related to count data, including overdispersion and underdispersion, making it a comprehensive resource for both theoretical understanding and practical application.