An Application of the Generalized Poisson Model for Over Dispersion Data on The Number of Strikes Between 1984 and 2017

Year 2020, , 249 - 260, 31.12.2020

Burcu Durmuş , Öznur İşçi Güneri

https://doi.org/10.17093/alphanumeric.670611

Cited By: 5

Abstract

Poisson regression analysis is widely used in many studies including count data. Poisson regression analysis is based on the assumption of equal mean and variance. However, this assumption is quite difficult in regression models. In cases where the assumption is not provided, over dispersion or under dispersion occurs. Over dispersion in data occurs when the variance of the dependent variable is greater than the average. This results in lower estimates than the standard errors. The generalized Poisson regression model is one of the methods used in case of over dispersion. This model is a generalization of Poisson regression. In this study, Poisson regression and generalized Poisson regression methods were used in the modeling of count data for determinants of strikes between 1984 and 2017. According to empirical results, while all explanatory variables of the Poisson regression model were significant, the unemployment rate was found to be insignificant for the generalized Poisson regression model. This result was evaluated considering the structure of the data.

Keywords

Generalized Poisson model, Count data, Overdispersion, Strike numbers

References

• Agresti, A. (1996). An Introduction to Categorical Data Analysis. John Wiley and Sons, New York.
• Akaike, H. (1973). “Information Theory and an Extension of the Maximum Likelihood Principle”, 2nd International Symposium on Information Theory, 267-281.
• Al-Ghirbal AS, Al-Ghamdi AS. 2006. Predecting severe accidents rates at roundabouts using Poisson distribution. TRB Annual Meeting, TRB Paper 06-1684.
• Cameron, A.C. and Trivedi, P. K., (1998). Regression Analysis of Count Data: Cambridge University Press.
• Deniz Ö. 2005. Poisson Regreyon Analizi, İstanbul Ticaret Üniv Fen Bilim Derg, 4(7): 59-72.
• Gujaratti DF. 1999. Temel Ekonometri, Çev. Ümit Şenesen ve Gülay Günlük Şenesen, İstanbul: Literatür Yayıncılık.
• Hoffman, John (2004), Generalized linear models, Boston, Pearson Education Inc.
• Hurvich, C.M. ve Tsai, C. (1989). “Regression and Time Series Model Selection in Small Samples”, Biometrika, 76, 297-307.
• Joe, H. and Zhu, R., (2005). Generalized Poisson Distribution: the Property of Mixture of Poisson and Comparison with Negative Binomial Distribution, Biometrical Journal 47(2),219‐229.
• Kibar, F.T. 2008. Trafik Kazaları ve Trabzon Bölünmüş Sahil Yolu Örneğinde Kaza Tahmin Modelinin Oluşturulması. Karadeniz Teknik Üniversitesi, Fen Bilimleri Enstitüsü, Yüksek Lisans Tezi.
• King, G. (1988). “Statistical Models for Political Science Event Counts: Bias in Conventional Procedures and Evidence for the Exponential Poisson Regression Model”, American Journal of Political Science, 3(3), 838-863.
• Koç, H., Cengiz, M.A., Koç, T., Dünder, E. “Aşırı Yayılımlı Veriler İçin Genelleştirilmiş Poisson Karma Modellerin Hava Kirliliği Üzerine Bir Uygulaması” International Anatolia Academic Online Journal(IAAOJ), Scientific Science, 2013, 1(2), 3‐7.
• Koutsoyiannis A. 1989. Ekonometri Kuramı, Çev: Ümit Şenesen ve Gülay Günlük Şenesen, Ankara: Verso yayıncılık.
• Moksony, F. ve Hegedus, R. (2014). “The Use of Poisson Regression in the Sociological Study of Suicide”, Corvinus Journal of Sociology And Social Policy, 5(2), 97-114.
• McQuarrie, A.D. ve Tsai, C. L. (1998). “Regression and Time Series Model Selection”, World Sciencetific.
• Osgood, D. Wayne (2000), “Poisson-Based Regression Analysis of Aggregate Crime Rates”, Journal of Quantitative Criminology, 16, 21–43.
• Pamukcu, E., Colak, C., Halısdemır, N. (2014) ”Modeling of The Number of Divorce in Turkey Using The Generalized Poisson, Quasi-Poisson and Negative Binomial Regression”, Turkish Journal of Science & Technology Volume 9(1), 89-96, 2014.
• Stata, (2019) StataCorp, Statistical software package, Stata v.14. Erişim linki: https://www.stata.com/
• Sugiuna, N. (1978). “Further Analysis of the Data by Akaike’s Information Criterion and the Finite Corrections”, Communication in Statistics-Theory and Methods, 57, 13-26.