Penerapan Metode Geographically Weighted Panel Regression (Gwpr) Pada Kasus Kemiskinan Di Indonesia

Martha, Shantika; Yundari, Yundari; Rizki, Setyo Wira; Tamtama, Ray

doi:10.30598/barekengvol15iss2pp241-248

Cited by 4 publications

(4 citation statements)

References 1 publication

(1 reference statement)

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“…where 𝑉𝐼𝐹 𝑘 is the value of the VIF of the 𝑘 independent variable and 𝑅 𝑘 2 is the 𝑘 coefficient of determination [8].…”

Section: Multicollinearity Detectionmentioning

confidence: 99%

“…The WLS method is a form of development of the Ordinary Least Square (OLS) method by considering spatial weighting at each observation location. Spatial weighting represents the magnitude of the influence of one data with other data [2]. This study uses the panel regression model and GWPR model to be applied to stunting toddler data in Indonesia.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Geographically Weighted Panel Regression Model With Bisquare Kernel Weighting Function on Percentage of Stunting Toddlers in Indonesia

Asnita,

Sifriyani,

Fauziyah

2024

BAREKENG: J. Math. & App.

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Stunting is a condition of failure to thrive in children under five years old due to chronic malnutrition. Efforts that can be made to reduce the incidence of stunting in Indonesia are to identify factors that are thought to affect the incidence of stunting in Indonesia. The analysis methods used in this study are the global Fixed Effect Model (FEM) and the local Geographically Weighted Panel Regression (GWPR) model. FEM is a global regression model that assumes that each individual's model has a different intercept value. While GWPR is a local regression model from FEM that considers aspects of geographic location, by repeating data at each observation location, different times, and using spatial data. The weighting function used in this study is fixed bisquare and adaptive bisquare. This study aims to obtain a GWPR model on the percentage of stunting toddlers in Indonesia in 2019 until 2022 with independent variables, namely the percentage of children receiving exclusive breastfeeding , the percentage of households that have access to proper sanitation , the average per capita health expenditure of the population for a month , the average length of schooling for women , and the number of poor people . The variables are obtained from Statistics Indonesia (BPS) and Study of Indonesia’s Nutritional Status (SSGI). The results showed that the best weighting function, namely adaptive bisquare with a CV value of 264.80.

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“…where 𝑉𝐼𝐹 𝑘 is the value of the VIF of the 𝑘 independent variable and 𝑅 𝑘 2 is the 𝑘 coefficient of determination [8].…”

Section: Multicollinearity Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Geographically Weighted Panel Regression Model With Bisquare Kernel Weighting Function on Percentage of Stunting Toddlers in Indonesia

Asnita,

Sifriyani,

Fauziyah

2024

BAREKENG: J. Math. & App.

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“…CV in GWPR is the same as GWR, calculated based on the average of the dependent and independent variables for the entire time [6]. CV calculation can be written as follows [12]:…”

Section: Weight Model Geographically Weighted Panel Regression With F...mentioning

confidence: 99%

Analysis of Spatial Effects on Factors Affecting Rice Production in Central Sulawesi Using Geographically Weighted Panel Regression

Gamayanti

Junaidi

Fadjryani

et al. 2023

BAREKENG: J. Math. & App.

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Fulfillment of rice stock in Indonesia to always be distributed based on demand in the community is certainly closely related to the results of rice production. The results of rice production in various regions of Indonesia are very different. This difference can of course be influenced by geographic location or spatial effects between regions. Central Sulawesi, which is one of the provinces with a large population compared to other provinces on the island of sulwesi, has a responsibility to meet the needs of its community, so it is necessary to take into account and increase the production of rice by relying on production in the province.Modeling of rice production that has spatial effects or heterogeneity between regions is needed as an analytical tool because if the modeling ignores spatial effects and generalizes the model, the modeling predictions will be biased. So we need an analytical model that can accommodate the problem of spatial effects using Geographically Weighted Panel Regression. The purpose of this study was to determine the factors that can affect rice production in central sulawesi. The data used comes from BPS Central Sulawesi province from 2014-2020. This study focus to the spatial effect factors that are considered to be able to affect the rice production production in Central Sulawesi. Tthe results of the study there area 8 districts/cities which are affected by land area, and 4 districts/cities are affected by land area and harvested are.

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“…Research that used the GWPR method in a case study of poverty in Indonesia was conducted by [6]. They obtained that the FEM with the function of the weighted exponential adaptive kernel is the GWPR model much better to be used.…”

Section: Introductionmentioning

confidence: 99%

Gender Inequality Index Modeling in Indonesia Using Geographically Weighted Panel Regression Method

Umi,

Junaidi,

Fadjryani

2023

Advances in Physics Research

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The Gender Inequality Index (GII) is an index that describes the failure of human development achievement due to the inequality of gender that is measured by several aspects such as health, empowerment, and labor market access. The Geographically Weighted Panel Regression (GWPR) is a developed model which amalgamates the GWR model with panel data regression. The GWPR is used to address spatial heterogeneity problems in panel data. This study aims to determine the results of the GII modeling in Indonesia using the GWPR and their several influenced factors. The results of the GII modeling using the GWPR with adaptive kernel bisquare weighted functions produce different model equations at each location. The GWPR model produced an R 2 of 73.97% with factors that significantly affect the GII including the percentage of people aged over 10 years who have no school (X 2 ), the percentage of non-health facilities of childbirth (X 3 ), the percentage of women sitting in parliament (X 4 ), DI (X 5 ), GDI (X 6 ), OUR (X 7 ) and LFPR (X 8 ). Moreover, given an example, by the assumption of other variables are ignored the GII of Central Sulawesi Province in the t-th year will decrease by 0.0296. Moreover, the increase of 1% of women sitting in parliament (X 4 ) in the t-th year by the assumption of ignoring other variables, will then decrease the GII by 0.0524. In addition, the increase of 1 unit of HDI (X 5 ) in the t-year by assuming other variables are ignored, will decrease the GII by 1.7063.

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Penerapan Metode Geographically Weighted Panel Regression (Gwpr) Pada Kasus Kemiskinan Di Indonesia

Cited by 4 publications

References 1 publication

Estimation of Geographically Weighted Panel Regression Model With Bisquare Kernel Weighting Function on Percentage of Stunting Toddlers in Indonesia

Estimation of Geographically Weighted Panel Regression Model With Bisquare Kernel Weighting Function on Percentage of Stunting Toddlers in Indonesia

Analysis of Spatial Effects on Factors Affecting Rice Production in Central Sulawesi Using Geographically Weighted Panel Regression

Gender Inequality Index Modeling in Indonesia Using Geographically Weighted Panel Regression Method

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