Pemodelan Regresi Spasial pada Tingkat Kemiskinan di Pulau Sulawesi
DOI:
https://doi.org/10.20956/ejsa.v6i1.40494Keywords:
Expenditure Per Capita, Life Expectancy, Moran Index, Spatial Autoregressive Model, Spatial Error ModelAbstract
In regression analysis, the independence assumption of the error terms is often violated when working with spatial data. The 2023 poverty incidence data across regencies/municipalities on Sulawesi Island indicate the presence of spatial autocorrelation. This study aims to compare the performance of classical regression, spatial autoregressive model (SAR), and spatial error model (SEM) in modeling poverty incidence on the island. The regency/municipality-level data used in the study is secondary data published by BPS-Statistics Indonesia. The findings reveal that the SEM model provides more accurate parameter estimates compared to classical regression and SAR model. Factors that have a significant influence on the poverty incidence (Y) in a regency/municipality are life expectancy (X1), expenditure per capita (X2), and the error terms for the nearest neighboring regions (λ).
References
BPS. Data dan Informasi Kemiskinan Kabupaten/Kota Tahun 2023. Jakarta: Badan Pusat Statistik, 2023.
Lawanson, O. I., & Umar, D. I. The Life Expectancy-Economic Growth Nexus in Nigeria: The Role of Poverty Reduction. SN Business & Economics, 1(127), 2021.
Hasanah, R., Syaparuddin, & Rosmeli. Pengaruh Angka Harapan Hidup, Rata-rata Lama Sekolah, dan Pengeluaran Perkapita terhadap Tingkat Kemiskinan pada Kabupaten/Kota di Provinsi Jambi. e-Jurnal Perspektif Ekonomi dan Pembangunan Daerah, 10(3), 2021.
Puteri, R. M., & Marwan. Pengaruh Pengangguran, Pengeluaran Per Kapita, Pendidikan dan Kesehatan terhadap Kemiskinan di Provinsi Sumatera Barat. Arzusin: Jurnal Manajemen dan Pendidikan Dasar, 3(3), 2023.
Qurrotu’aini, L. & Purbadharmaja, I. B. P. The Effect of Minimum Wage and Per Capita Expenditure on Poverty in Provincial Districts D.I. Yogyakarta. Jurnal Syntax Admiration, 4(9), 2023.
Liu, M., Ge, Y., Hu, S., Stein, A., & Ren, Z. The Spatial-Temporal Variation of Poverty Determinants. Spatial Statistics, 50(100631), 2022.
Djami, R. J., & Haumahu, G. Pemodelan Indeks Pembangunan Manusia di Provinsi Maluku dan Maluku Utara dengan Menggunakan Spatial Autoregressive Model (SAR). Variance: Journal of Statistics and Its Applications, 5(1), 2023.
Nurjanah, Rinaldi, A., & Pitri, R. Spatial Error Model pada Tingkat Kemiskinan Kabupaten/Kota di Provinsi Lampung. Variance: Journal of Statistics and Its Applications, 5(1), 2023.
Huda, N. M., & Imro’ah, N. Determination of the Best Weight Matrix for the Generalized Space Time Autoregressive (GSTAR) Model in the Covid-19 Case on Java Island, Indonesia. Spatial Statistics, 54(100734), 2023.
Baharuddin, Yahya, I., & Ihwal, M. Otokorelasi Spasial pada Prevalensi Balita Stunting, Wasting, Underweight, dan Overweight di Pulau Sulawesi Tahun 2022. Journal of Mathematics, Computation and Statistics, 7(2), 472-482, 2024.
BPS. Indeks Pembangunan Manusia 2023. Jakarta: Badan Pusat Statistik, 2024.
Yasin, H., Hakim, A. R., & Warsito, B. Regresi Spasial: Aplikasi dengan R. Pekalongan: Wade Group, 2020.
Shi, K., Chang, Z., Chen, Z., Wu, J., & Yu, B. Identifying and Evaluating Poverty Using Multisource Remote Sensing and Point of Interest (POI) Data: A Case Study of Chongqing, China. Journal of Cleaner Production, 255(120245), 2020.
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