Geographically Weighted Panel Regression with Fixed Effect Model: Spatio-Temporal Modeling of Poverty Rates in Central Java 2020-2023.

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Title:	Geographically Weighted Panel Regression with Fixed Effect Model: Spatio-Temporal Modeling of Poverty Rates in Central Java 2020-2023.
Authors:	Putra, Robiansyah¹ robiansyah@usu.ac.id, Fadhlurrahman, Muhammad Ghani² mghanif1109@gmail.com, Tyas, Sischa Wahyuning³ sischa_wahyuning.sada@upnjatim.ac.id
Source:	IAENG International Journal of Applied Mathematics. Mar2026, Vol. 56 Issue 3, p1006-1019. 14p.
Subjects:	Fixed effects model, Spatiotemporal processes, Poverty rate, Indonesians, Provinces
Geographic Terms:	Indonesia, Jawa Tengah (Indonesia)
Abstract:	Poverty remains a serious issue in Indonesia, particularly in Central Java Province. The poverty rate in Central Java has consistently been above the national average and is the highest in the Java-Bali region after the Special Region of Yogyakarta (DIY). The factors influencing the poverty rate in Central Java over a given period can be analyzed using the Geographically Weighted Panel Regression (GWPR) model. Based on the results, the GWPR model with the fixed exponential weighting method achieved a coefficient of determination of 93.71%, whereas the Fixed Effect Model (FEM) panel data regression, as a global regression model, attained only 49.49%. This indicates that the GWPR model provides a more optimal fit for analyzing poverty rates in Central Java during the 2020-2023 period compared to the FEM panel data regression. Furthermore, the GWPR model reveals that each district has distinct influencing factors due to spatial effects. For instance, in Wonosobo, the factors affecting the poverty rate are X2, X3, X5, X6, and X7, whereas in Pati, only X6 has a significant effect. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

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Abstract:	Poverty remains a serious issue in Indonesia, particularly in Central Java Province. The poverty rate in Central Java has consistently been above the national average and is the highest in the Java-Bali region after the Special Region of Yogyakarta (DIY). The factors influencing the poverty rate in Central Java over a given period can be analyzed using the Geographically Weighted Panel Regression (GWPR) model. Based on the results, the GWPR model with the fixed exponential weighting method achieved a coefficient of determination of 93.71%, whereas the Fixed Effect Model (FEM) panel data regression, as a global regression model, attained only 49.49%. This indicates that the GWPR model provides a more optimal fit for analyzing poverty rates in Central Java during the 2020-2023 period compared to the FEM panel data regression. Furthermore, the GWPR model reveals that each district has distinct influencing factors due to spatial effects. For instance, in Wonosobo, the factors affecting the poverty rate are X2, X3, X5, X6, and X7, whereas in Pati, only X6 has a significant effect. [ABSTRACT FROM AUTHOR]
ISSN:	19929978