Pemodelan Data Kemiskinan Provinsi Jawa Tengah Menggunakan Fixed Effect Spatial Durbin Model

Abstract

Based on data from the Central Statistics Agency, Central Java has 4,20 million people (12,23%) poor population in 2017 with Rp333.224,00 per capita per month poverty line. So, Central Java has got the second rank after East Java as the province which has the highest poor population in indonesia in 2017. In this research use the fixed effects spatial durbin model method for modeling poor population in each city in Central Java at 2014-2017. The spatial durbin model is a spatial regression model which contains a spatial dependence on dependent variable and independent variable. If the spatial dependence on dependent variable or independent variables is ignored, the resulting coefficient estimator will be biased and inconsistent. The fixed effect is one of the panel data regression models which assumes a different intercept value at each observation but fixed at each time, and slope coefficient is constant. The advantage of using fixed effects in spatial panel data regression is able to know the different characteristics in each region. The dependent variable used is poor population in each city in Central Java, and the independent variable is Minimum Wage, Life Expectancy, School Participation Rate 16-18 Years, Expected Years of Schooling, Total Population, and Per Capita Expenditure. The results of the analysis shows that the fixed effects spatial durbin model is significant and can be used. The variables that significantly affect the model are the Life Expectancy and Expected Years of Schooling, and the coefficient of determination (R2) is 99.95%. Keywords: Poverty, Spatial, Panel Data, Fixed Effects Spatial Durbin Model

Spatial Spillover Impact of Sectoral Government Expenditure on Poverty Alleviation in South Kalimantan Province

“…Aklilu (2015) performed Spatial Lag Model (SLM) to investigate poverty and its determining factors in Java. Meanwhile, considering the spatial dependency on the independent and dependent variables, Alvitiani et al (2019) performed Spatial Durbin Model (SDM) to analyze poverty in Central Java. In contrast, in analyzing farmers' poverty in Jambi, Nashwari et al (2017) utilized Geographically Weighted Regression (GWR) that considers the spatial deference among observations.…”

Regresi Data Panel Untuk Pemodelan Jumlah Penderita Tuberculosis di Kabupaten Bojonegoro

“…Untuk pengujian homoskedastisitas menggunakan uji Gletjer. Apabila hasil P_value > α atau F-hitung < F-tabel maka asumsi homoskedastisitas terpenuhi (Alvitiani et al, 2019).…”

“…Untuk uji simultan, statistik uji yang digunakan adalah F-hitung atau P_value. Apabila F-hitung > F-tabel atau P_value < α maka kesimpulannya adalah variabel prediktor secara keseluruhan berpengaruh terhadap variabel respon(Alvitiani et al, 2019). ii) Uji parsial digunakan untuk mendapatkan variabel prediktor yang secara signifikan berpengaruh terhadap variabel respon secara parsial.…”

Pemodelan Data Kemiskinan di Provinsi Jawa Barat Menggunakan Regresi Spasial Data Panel

“…d. Melakukan pendugaan parameter Fixed Effect Spatial Error Model (SEM) (Elhorst, 2014). e. Melakukan uji signifikansi yang terdiri dari uji Likelihood Ratio (Elhorst, 2014) dan uji signifikansi parameter (uji T) (Alvitiani, Yasin and Mukid, 2019). f. Menguji asumsi yang harus dipenuhi, diantaranya yaitu uji normalitas, uji homoskedastisitas , uji autokorelasi, dan uji multikolinearitas.…”