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Pravitasari, Anindya Apriliyanti
Padjadjaran University
Computer Science & IT Decision Sciences, Operations Research & Management Engineering Mathematics Social Sciences

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Implementation of Spatial Autoregressive with Autoregressive Disturbance (SARAR) using GMM to Identify Factors Caused Poverty in West Java Ningtias, Yunita Dwi Ayu; Andriyana, Yudhie; Pravitasari, Anindya Apriliyanti
Inferensi Vol 7, No 2 (2024)
Publisher : Department of Statistics ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j27213862.v7i2.20309

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.
Co-Authors Ningtias, Yunita Dwi Ayu Yudhie Andriyana