Pemodelan Persentase Penduduk Miskin di Provinsi Kalimantan Timur Menggunakan Model Geographically Weighted Panel Regression

Authors

  • Arjun Pratama Universitas Mulawarman
  • Suyitno Suyitno Universitas Mulawarman
  • Ika Purnamasari Universitas Mulawarman

DOI:

https://doi.org/10.24252/msa.v9i2.21021

Abstract

Model Geographically Weighted Panel Regression (GWPR) merupakan pengembangan model regresi panel yang diaplikasikan pada data spasial. Model GWPR yang dibahas pada penelitian ini adalah Fixed Effect Model (FEM) spasial. Penaksiran parameter model GWPR dilakukan pada setiap lokasi pengamatan menggunakan pembobot spasial. Tujuan penelitian ini adalah mengetahui faktor-faktor yang mempengaruhi persentase penduduk miskin tahun 2017-2019 di 10 Kabupaten/Kota Provinsi Kalimantan Timur. Penaksiran parameter model GWPR menggunakan metode Weighted Least Square (WLS). Pembobot spasial pada penaksiran parameter ditentukan menggunakan fungsi pembobot terbaik di antara pembobot fixed gaussian, fixed bisquare, fixed tricube, dan adaptive bisquare. Penentuan bandwidth optimum menggunakan kriteria Cross Validation (CV) dan diperoleh fungsi pembobot dengan nilai CV minimum adalah adaptive bisquare. Berdasarkan hasil pengujian parameter model GWPR, disimpulkan bahwa faktor-faktor yang berpengaruh terhadap persentase penduduk miskin bersifat lokal dan berbeda-beda di 10 Kabupaten/Kota di Provinsi Kalimantan Timur. Faktor-faktor yang berpengaruh dan bersifat lokal terhadap persentase penduduk miskin adalah indeks pembangunan manusia, umur harapan hidup, pendapatan domestik regional bruto, tingkat partisipasi angkatan kerja, tingkat pengangguran terbuka, dan laju pertumbuhan penduduk. Berdasarkan nilai koefisien determinasi dan Root Mean Square Error (RMSE) diperoleh bahwa model GWPR lebih baik daripada model regresi panel global dengan nilai koefisien determinasi sebesar 94,40% dan nilai RMSE sebesar 0,0451.

Kata Kunci : Adaptive Bisquare, CV, GWPR, Persentase Penduduk Miskin, RMSE

References

[1] Badan Pusat Statistik. (2020). Berita Resmi Statistik: Profil Kemiskinan di Indonesia Maret 2020

[2] Worldbank. (2009). Handbook on Poverty and Inequality. Washington DC: The World Bank

[3] Henninger, N., dan Snel, M. (2002). Where are the Poor? Experience with the Development and Use of Poverty Maps. Arendal: World Resources Institute and UNEP/GRID

[4] Qur'ani, A. Y. (2014). Pemodelan Geographically Weighted Regression Panel (GWR-Panel) Sebagai Pendekatan Model Geographically Weighted Regression (GWR) dengan Menggunakan Fixed Effect Model Time Trend. Jurnal Mahasiswa Statistik. 2(3)

[5] Hsiao, C. (2003). Analysis of Panel Data. New York: Cambridge University Press

[6] Badan Pusat Statistik Provinsi Kalimantan Timur. (2020). Berita Resmi Statistik: Profil Kemiskinan di Kalimantan Timur Maret 2020

[7] Greene. W. H. (2007). Econometrics Analysis 6th Edition. New Jersey: Prentice Hall

[8] Wooldridge, J. M. (2002). Econometric Analysis of Cross-section and Panel Data. London: MIT Press

[9] Meutuah, M. S., Yasin H., dan Maruddani D.A.I. (2017). Pemodelan Fixed Effect Geographically Weighted Panel Regression Untuk Indeks Pembangunan Manusia Di Jawa Tengah. Jurnal Gaussian. 6(2). 241-250

[10] Prasanti, T. A. (2015). Aplikasi Regresi Data Panel untuk Pemodelan Tingkat Pengangguran Terbuka Kabupaten/Kota di Provinsi Jawa Tengah. Jurnal Gaussian, 4(3), 687-696

[11] Setiawan dan Kusrini. (2010). Ekonometrika. Yogyakarta: Penerbit ANDI

[12] Kutner, M.H., Nachtsheim, C.J., Neter, J., & Li, W. (2005). Applied Linear Statistical Model 5th Edition. New York: McGraw-Hill

[13] Gujarati, D. N. (2003). Ekonometrika Dasar. Jakarta: Erlangga

[14] Pindyck, R.S. dan D.I. Rubinfeld. (1998). Econometric Models and Economic Forecast. 4th Edition. McGraw-Hill Int’l Edition

[15] Gujarati, D. N. (2004). Basic Econometrics Fourth Edition. New York: The McGraw-Hill Companies

[16] Widarjono. (2009). Ekonometrika Pengantar dan Aplikasinya, Edisi Ketiga. Yogyakarta: Ekonisia

[17] Yasin, H. (2011). Pemilihan Variabel pada Model Geographically Weighted Regression. Jurnal. Program Studi Statistika FMIPA UNDIP.

[18] Fotheringham, A.S., Brunsdon, C., dan Charlton, M. (2002). Geographically Weighted Regression : the Analysis of Spatially Varying Relationships. Chichester: Wiley

[19] Bruna, F., dan Yu, D. (2013). Geographically Weighted Panel Regression. XI Congreso Galego de Estatica e Investigacion de Operations. A Coruna

[20] Wei, W.W.S. (2006). Time Series Analysis: Univariate and Multivariate Methods Second Edition. New Jersey: Pearson Prentice Hall.s

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Published

2021-12-28

How to Cite

[1]
A. Pratama, S. Suyitno, and I. Purnamasari, “Pemodelan Persentase Penduduk Miskin di Provinsi Kalimantan Timur Menggunakan Model Geographically Weighted Panel Regression”, MSA, vol. 9, no. 2, pp. 1–11, Dec. 2021.

Issue

Vol. 9 No. 2 (2021): VOLUME 9 NOMOR 2 TAHUN 2021

Section

Artikel

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