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All Journal Al-Khwarizmi: Jurnal Pendidikan Matematika dan Ilmu Pengetahuan Alam Jurnal Varian ESTIMASI: Journal of Statistics and Its Application
Raupong, Raupong
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Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Mathematics Social Sciences Other

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Journal : Jurnal Varian

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Panel Data Regression Modeling with Weighted Least Squares Method Using Fair Weights Ferdiansyah, Muhammad; Raupong, Raupong; Siswanto, Siswanto
Jurnal Varian Vol. 8 No. 2 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i2.4392

Abstract

Panel data regression is a robust method for analyzing relationships between dependent and independent variables by combining time-series and cross-sectional data. Its reliability hinges on key assumptions, particularly homoscedasticity. Violations, known as heteroscedasticity, lead to inefficient estimates and biased inference, as estimators fail to meet the Best Linear Unbiased Estimator criteria. The Weighted Least Squares (WLS) method addresses heteroscedasticity by weighting observations based on the inverse of their variance. WLS assumes prior knowledge of the heteroscedasticity structure, which is often impractical, creating gap in evaluating its effectiveness compared to alternative methods. The purpose of this study is to examines life expectancy in South Sulawesi as the dependent variable, with expected years of schooling, per capita expenditure, and average years of schooling as independent variables. The research methode used WLS with reasonable weighting, successfully addressing heteroscedasticity. The fixed-effects model was identified as the most appropriate, with an R-squared of 99.45%. Life expectancy was explained by the model. Results shows all variables positively and significantly influence life expectancy. In conclusion, the WLS method effectively overcomes heteroscedasticity in panel data regression, providing reliable estimators. This study highlights the importance of method selection in panel data analysis and offers insights for policymakers aiming to improve life expectancy in South Sulawesi.
Mixed Geographically Weighted Regression Modeling Using the MM-Estimator Method on Data of Poverty Amalia Mentari Djalumang; Raupong, Raupong; Siswanto, Siswanto
Jurnal Varian Vol. 8 No. 3 (2025)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/varian.v8i3.5146

Abstract

The mixed geographically weighted regression model combines a global linear regression model with a geographically weighted regression model, with some parameters global and others local. When analyzing data with this model, outliers are common, which can significantly affect the regression coefficients and lead to biased parameter estimates. Therefore, a more robust estimation method that is resistant to outliers is needed to improve accuracy. This study aims to estimate the parameters of the mixed geographically weighted regression model using the Method of Moments (MM) Estimator method, which is more robust to outliers, and to identify the factors that significantly influence the percentage of the poor population in South Sulawesi Province in 2023. The results show that the poverty depth index has a significant global effect on the percentage of the population living in poverty. Meanwhile, the percentage of the population, the open unemployment rate, and the expected years of schooling have significant local effects. Based on these findings, it can be concluded that neighboring regions share common factors influencing poverty rates. These findings can assist policymakers in designing povertyalleviation programs that account for regional differences and support further research on robust spatial modeling approaches. 
Co-Authors Amalia Mentari Djalumang Angriany, A.Muthiah Nur Anisa Anisa Anna Islamiyati Asnidar Asnidar Jusmawati Massalesse Muhammad Fadil Muhammad Ferdiansyah, Muhammad Nirwan Ilyas Nurhidayah, Fauziah Nurul Hikmah S Siswanto Sahriman, Sitti Sirajang, Nasrah Wahyuni, Alfiana