Implementation of Spatial Autoregressive with Autoregressive Disturbance (SARAR) using GMM to Identify Factors Caused Poverty in West Java

Yunita Dwi Ayu Ningtias, Yudhie Andriyana, Anindya Apriliyanti Pravitasari

Abstract


Poverty is one of the crucial problems that has a negative impact on all sectors. As a developing country, Indonesia has a fairly high poverty rate. The government's efforts to overcome the problem of poverty can be circumvented by detecting the factors that influence it to determine the policies taken by using statistical modeling. There is a spatial effect on poverty in West Java Province. Spatial Data Analysis is the only statistical model that can explain the relationship between an area and the surrounding area. If the response variable contains a lag that correlates with each other, it is called a Spatial Autoregressive with Autoregressive Disturbances (SARAR) model. The Generalized Method of Moment (GMM) approach is used to get an estimator from the model. This method is applied to obtain the factors that influence poverty in West Java Province. The results of this study indicate that the GMM SARAR poverty modeling with customized weights provides relatively better estimation results. In addition, the relationship between locations (spatial lag dependence) is positive and significant. Expected Years of Schooling and Per capita Expenditure have a negative and significant effect on the increase in the percentage of poor people in West Java.

Keywords


Spatial Data Analysis, SARAR, GMM, Poverty

Full Text:

PDF

References


BPS, Perkembangan Tingkat Kemiskinan Provinsi Jawa Barat September 2020, Bandung: Badan Pusat Statistik Provinsi Jawa, 2021b.

SDGs, "Sustainable Development Goals," 2021. [Online]. Available: https://www.sdg2030indonesia.org/.

Localise SDGs-Indonesia, "Profil Daerah Provinsi Jawa Barat," 2022. [Online]. Available: https://localisesdgs-indonesia.org/profil-tpb/profil-daerah/11.

RPJMD, Rancangan Akhir Rencana Pembangunan Jangka Menengah Daerah (RPJMD) Provinsi Jawa Barat Tahun 2018-2023, Bandung: Bappeda Jawa Barat, 2020.

L. Anselin, Spatial Econometrics: Methods and Models, Netherlands: Springer, 1988.

H. Isfahani, Y. Suparman and R. Arisanti, "Fixed Effect Spatial Error Model untuk Mengidentifikasi Faktor-Faktor Tingkat Kemiskinan di Jawa Barat," Seminar Nasional Statistika IX , pp. 2599-2546, 2020.

S. Yulianto, "Pemodelan regresi spasial pada tingkat kemiskinan Provinsi Jawa Barat," Seminar Nasional Matematika Dan Pendidikan Matematika (5th senatik), 2020.

E. Yulian, "Penanganan Endogenitas Modal Sosial Pada Pemodelan Kemiskinan Rumah Tangga di Indonesia dengan Metode Two Probit Least Square (2PLS)," Jurnal Fourier, vol. 8, pp. 19-26, 2019.

J. P. Elhorst, Spatial Econometrics: From Cross-Sectional Data to Spatial Panels, New York: Springer Heidelberg, 2014.

J. P. Lesage and R. K. Pace, "Spatial and Spatiotemporal Econometrics," in Instrumental Variable Estimation of A Spatial Autoregressive Model with Autoregressive Disturbances: Large and Small Sample Results, Elsevier Ltd., 2004, p. 163.

D. M. Drukker, I. R. Prucha and R. Raciborski, "A command for estimating spatial-autoregressive models with spatial-autoregressive disturbances and additional endogenous variables," The Stata Journal, vol. 13, pp. 287-301, 2013a.

R. Bivand and G. Piras, "Comparing Implementations of Estimation Methods for Spatial conometrics," Journal of Statistical Software, vol. 63(18), pp. 1-36, 2015.

I. Arraiz, D. M. Drukker, H. H. Kelejian and I. R. Prucha, "A Spatial Cliff-Ord-type Model with Heteroskedastic Innovations: Small and Large Sample Results," CESifo Working Paper No.2485, 2008.

H. H. Kelejian and I. R. Prucha, "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," Journal of Real Estate Finance and Economics, vol. 17:1, pp. 99-121, 1998.

H. H. Kelejian and I. R. Prucha, "Specification and Estimation of Spatial Autoregressive Models with Autoregressive and Heteroskedastic Disturbances," National Institutes of Health, vol. 157(1), pp. 53-67, 2010.

R. Bivand, G. Millo and G. Piras, "A Review of Software for Spatial Econometrics in R," Mathematics, vol. 9, p. 1276, 2021.

L. Anselin, "GMM Estimation of Spatial Error Autocorrelation with and without Heteroskedasticity," 2011.

D. M. Drukker, P. Egger and I. R. Prucha, "On Two-Step Estimation Of A Spatial Autoregressive Model With Autoregressive Disturbances And Endogenous Regressors," Econometric Reviews, Vols. 32(5-6), pp. 686-733, 2013b.

M. P. Todaro and S. C. Smith, Pembangunan Ekonomi, Jakarta: Erlangga, 2010.

H. Siregar and D. Wahyuniarti, "Dampak Pertumbuhan Ekonomi terhadap Penurunan Jumlah Penduduk Miskin," MB-Institut Pertanian Bogor, 2007.

R. Hasanah, S. and R. , "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, vol. 10 (3), 2021.

Kementerian PPN/Bappenas, "Sustainable Development Goals," 2021. [Online]. Available: https://sdgs.bappenas.go.id/.

BPS, Profil Kemiskinan Jawa Barat Maret 2020, Bandung: Badan Pusat Statistik Provinsi Jawa Barat, 2020d.

BPS, Profil Kemiskinan di Indonesia Maret 2020, Indonesia: Badan Pusat Statistik, 2020c.

A. F. I. Shina, "Estimasi Parameter pada Sistem Model Persamaan Simultan Data Panel Dinamis dengan Metode 2 SLS GMM-AB," Media Statistika, vol. 11(2), pp. 79-91, 2018.




DOI: http://dx.doi.org/10.12962/j27213862.v7i2.20309

Refbacks

  • There are currently no refbacks.


Bookmark and Share


Creative Commons License
Inferensi by Department of Statistics ITS is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Based on a work at https://iptek.its.ac.id/index.php/inferensi.

ISSN:  0216-308X

e-ISSN: 2721-3862

Web Analytics Made Easy - StatCounter View My Stats