DOI: https://doi.org/10.14710/medstat.16.1.47-58

GEOGRAPHICALLY WEIGHTED PANEL LOGISTIC REGRESSION SEMIPARAMETRIC MODELING ON POVERTY PROBLEM

Aliyah Husnun Azizah  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
*Nurjannah Nurjannah  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
Adji Achmad Rinaldo Fernandes  -  Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Indonesia
Rosita Hamdan  -  Department of Development Economics, University Malaysia Serawak, Malaysia
Open Access Copyright (c) 2023 MEDIA STATISTIKA under http://creativecommons.org/licenses/by-nc-sa/4.0.

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Abstract
Regression analysis is a statistical method used to investigate and model the relationship between variables. Furthermore, a regression analysis was developed that involved spatial aspects, namely Geographically Weighted Regression (GWR). GWR modeling consists of various types, one of which is Geographically Weighted Logistic Regression Semiparametric (GWLRS), an extension of the Logistic GWR model that produces local and global parameter estimators. In this study, it is proposed to combine the GWLRS model using panel data or Geographically Weighted Panel Logistic Regression Semiparametric (GWPLRS). The case study used in this research is the problem of poverty in 38 regions/cities in East Java, Indonesia, in 2018 – 2022 as seen from the Poverty Gap Index. The weights used in this research are the adaptive gaussian kernel weighting functions. The results of the parameter significance test show that the Human Development Index as global variable has a significant effect on each region/city.
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Keywords: Geographically Weighted Regression; Geographically Weighted Panel Logistic Regression Semiparametric; Poverty Gap Index.

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Language : EN
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  1. Agustin, N., Hidayat M. S., & Umiyati, E. 2019. Analisis Pengaruh Pertumbuhan Ekonomi, Indeks Pembangunan Manusia (IPM) dan Upah Minimum Provinsi (UMP) terhadap Kemiskinan di Kabupaten Merangin. E-Jurnal Perspektif Ekonomi dan Pembangunan Daerah, 8(01)
  2. Anselin, L. 1988. Spatial Econometrics: Methods and Models. Kluwer Academic Publishers
  3. Apriliani, A. R., Safrida, & Fajri. 2023. Pengaruh Pengangguran terhadap Indeks Kedalaman Kemiskinan di Provinsi Aceh. Jurnal Ilmiah Mahasiswa Pertanian, 38(1)
  4. Atkinson, P. M., German, S. E., Sear, D. A., & Clark, M. J. 2003. Exploring the relations between riverbank erosion and geomorphological controls using geographically weighted logistic regression. Geographical Analysis, 35(1), 58-82
  5. Baltagi, B.H. 2005. Econometric Analysis of Panel Data, 3rd Edition. John Wiley & Sons, Ltd
  6. BPS, “Kemiskinan dan Ketimpangan [Poverty and Inequality].” Badan Pusat Statistik [Central Bureau of Statistics]. https://www.bps.go.id/subject/23/kemiskinan-dan-ketimpangan.html (accessed March. 27,2023)
  7. BPS, “Poverty and Human Development,” in Statistik Indonesia, Statistical Yearbook of Indonesia 2022, BPS-Statistics Indonesia, 2022
  8. BPS, Jawa Timur Province in Figures 2022. Surabaya: Central Bureau of Statistics; 2022
  9. Caraka, R.E., & Yasin, H. 2017. Geographically Weighted Regression (GWR) Sebuah Pendekatan Regresi Geografis. Yogyakarta : MOBIUS
  10. Fitriani, R. Effendi, A. 2019. Ekonometrika Spasial Terapan dengan R. Malang : UB Press
  11. Fotheringham, A.S., Brunsdon, C., & Charlton, M. 2002. Geographically Weighted Regression, The Analysis of Spatially Varying Relationships. John Wiley & Sons, Ltd
  12. Gujarati, D.N. 2004. Basic Econometrics, 4th Edition. McGraw-Hill/Irwin
  13. Hendayanti, N. P. N., & Nurhidayati, M. 2020. Regresi Logistik Biner dalam Penentuan Ketepatan Klasifikasi Tingkat Kedalaman Kemiskinan Provinsi-Provinsi di Indonesia. Sainstek: Jurnal Sains dan Teknologi, 12(2), 63-70
  14. Nakaya, T., Fotheringham, S., Charlton, M., & Brunsdon, C. 2009. Semiparametric geographically weighted generalised linear modeling in GWR 4.0
  15. Wiriarsa, M., Papyrakis, E., Hoeven, R. 2016. Upah Minimum dan Kemiskinan Studi Empiris pada Data Panel Level Kabupaten Kota di Jawa Indonesia = Minimum Wage and Poverty Empirical Study on District Panel Data in Java Indonesia. Tesis. Fakultas Ekonomi dan Bisnis Universitas Indonesia. Depok

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