ALTERNATIVE MODELS IN OVERCOMING THE PROBLEM OF OVERDISPERSION IN POISSON REGRESSION

Pardomuan Robinson Sihombing (BPS-Statistics Indonesia)
Ade Marsinta Arsani (BPS-Statistics Indonesia)
Ni Komang Semara Yanti (Udayana University)
Putu Pande Wahyu Diatmika (Udayana University)

Jurnal TAMBORA
Vol. 7 No. 2 (2023): EDISI 19

This study aims to compare various alternative models in overcoming the problem of overdispersion in Poisson regression modeling. The comparative modeling is the Generalized Poisson model, Negative Binomial, and Generalized Negative Binomial. Modeling is applied to modeling the number of poor people in Central Java in 2021 with unemployment, HDI, and GRDP as independent variables. The results obtained by Generalized Poison are better than Negative Binomial and Generalized Negative Binomial because of the smaller AIC and BIC values ??and the larger R2. For simultaneous tests, it can be concluded that unemployment, HDI, and GRDP significantly affect the number of poor people. Only unemployment and HDI variables partially affect the number of poor people in Central Java. On the other hand, there is not enough evidence that GRDP affects some poor people. There is a need for comprehensive and relevant policies to overcome the number of poor people in an area.