Regression methods for the analysis of count data. Generalised linear models for limited dependent variables (eBook, PDF)
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Seminar paper from the year 2019 in the subject Business economics - Miscellaneous, grade: 1.0, Zeppelin University Friedrichshafen, course: Advanced Methods N Limited Dependent Variables, language: English, abstract: This paper assesses the application of regression methods to analyse count data. R-Code and Data are available from the author! While the common multiple regression method has a wide range of applicability, and can be accommodated to various different kinds of regressor variables, its application is limited to the modelling of response variables from the space of real numbers...
Seminar paper from the year 2019 in the subject Business economics - Miscellaneous, grade: 1.0, Zeppelin University Friedrichshafen, course: Advanced Methods N Limited Dependent Variables, language: English, abstract: This paper assesses the application of regression methods to analyse count data. R-Code and Data are available from the author! While the common multiple regression method has a wide range of applicability, and can be accommodated to various different kinds of regressor variables, its application is limited to the modelling of response variables from the space of real numbers. For the analysis of other kinds of responses, such as counts, a more generalised set of tools is needed. This toolset is given by the generalised linear model framework and maximum likelihood estimation. For the specific purpose of this paper, the count data analysis methods of Poisson, Negative-Binomial, Hurdle and Zero-Inflation models are considered. This paper explains their theoretical background and applies them to a unique dataset that motivates their respective use. It is structured as follows: section 2 describes the applied dataset and section 3 the generalised linear model framework. In section 4 and section 5 the basic count data models and their results are discussed, while section 6 and section 7 explain the more advanced methods and their results. section 8 concludes.
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