Regression methods for the analysis of count data. Generalised linear models for limited dependent variables


Term Paper (Advanced seminar), 2019

11 Pages, Grade: 1.0


Abstract or Introduction

This paper assesses the application of regression methods to analyse count data.

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.

Details

Title
Regression methods for the analysis of count data. Generalised linear models for limited dependent variables
College
Zeppelin University Friedrichshafen
Course
Advanced Methods | N | Limited Dependent Variables
Grade
1.0
Author
Year
2019
Pages
11
Catalog Number
V1003006
ISBN (eBook)
9783346380128
Language
English
Notes
Code and data are available on request.
Tags
Econometrics, Data Analysis, Limited Dependent Variables, Poisson-Regression, Regression, Count Data, Hurdle Model, Negative-Binomial Regression, Zero-Inflation Model, Maximum Likelihood
Quote paper
Martin Georg Haas (Author), 2019, Regression methods for the analysis of count data. Generalised linear models for limited dependent variables, Munich, GRIN Verlag, https://www.grin.com/document/1003006

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