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Panji Jiblathar
UIN Sulthan Thaha Saifuddin Jambi
Economics, Econometrics & Finance Education Environmental Science Law, Crime, Criminology & Criminal Justice Social Sciences

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SAR probit regression modeling using the fisher scoring approach: A case study of poverty levels on the island of Sumatra Panji Jiblathar; Germansah Germansah; Fadhlul Mubarak; Ellys Agustina; Nabilla Rida Tri Nisa
Priviet Social Sciences Journal Vol. 6 No. 4 (2026): April 2026
Publisher : Privietlab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55942/pssj.v6i4.1727

Abstract

The probit model is commonly used to study categorical response data. However, failing to account for spatial autocorrelation factors between regions can lead to inconsistent and biased parameter estimation results. This study focuses on examining the parameter estimation of the Spatial Autoregressive (SAR) Probit model through the Maximum Likelihood Estimation (MLE) method using the Fisher Scoring numerical iteration scheme. The model was implemented on poverty data across 131 regencies/cities in the Sumatra region for 2022. Empirical findings indicate that the GRDP growth rate, open unemployment rate, per capita expenditure, and expected years of schooling significantly affect the poverty rate. The results of this model development provide a prediction accuracy of 83.97%. This achievement is superior to that of the RIS Simulator technique, which only yields an accuracy of 74.05%. These results emphasize the advantages of the efficiency and accuracy of the Fisher Scoring approach in representing spatial dependencies in the poverty phenomenon in Sumatra.
Co-Authors Ellys Agustina Fadhlul Mubarak Germansah Germansah Nabilla Rida Tri Nisa