الفهرس 
Title :A Comparative Study of Estimation Methods for Count Panel Data Models
ContentsOnly 14 pages are availabe for public view
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Abstract
In general, panel data aims to studythe time dimensionrepresented by (Time-Series) and the sectoral dimensionrepresented by (Cross-Section), in order to achieve the maximum benefit from the data and toreach a better statistical analysis of this data,that could not be achieved in either cross-sectional or time-series alone. Therefore, paneldata has become one of the most important fields in econometrics literaturebecause of the new sources of data which observes the cross-sections of individuals over time. Furthermore,the researchersuse panel data models because they allow great flexibility in modelingbehavior between across individuals. Thecount panel data (CPD)analysis was used with increasing frequency in empirical research in economics, social sciences, and medicine, where thedependentvariable is not normally distributed and takes nonnegative integer valuesover several time periodsfor cross-sectional units.There are many examples in econometrics, political science, biological, and health scienceon this type of data.For example, thenumber of patents (PATE) that have been presented over time by many countries or companies.This thesis aims to studythe CPDmodels and some methods of estimatingunder different statistical assumptions.This thesis provides two estimation methods: the maximum likelihood estimation (MLE) and the conditional maximum likelihood (CML)estimation to estimatethe models: fixed effects Poisson(FEP), random effects Poisson(REP), fixed effects negative binomial (FENB), and random effects negative binomial (RENB)