scholarly journals The Step Construction of Geographically Weighted Panel Regression in Air Polluter Standard Index (APSI) Data

2018 ◽  
Vol 73 ◽  
pp. 12006
Author(s):  
Budi Warsito ◽  
Hasbi Yasin ◽  
Dwi Ispriyanti ◽  
Arief Rachman Hakim
Keyword(s):  
Regression Model ◽  
Weighting Function ◽  
Weighted Least Squares ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Local Linear Regression ◽  
Coefficient Of Determination ◽  
Panel Regression ◽  
Standard Index ◽  
Effect Model

Geographically Weighted Panel Regression or GWPR is a local linear regression model that combines GWR model and panel data regression model with considering spatial effect, especially spatial heterogeneity problem. This article is focused on the soft computation of GWPR model using Fixed Effect Model (FEM). Parameter estimation in GWPR is obtain by Weighted Least Squares (WLS) methods and the resulting model for each location will be different from one to another. This study will compare the fixed-effect GWPR model with several weighting functions. The best model is determined based on the biggest coefficient of determination (R2) value. In this study, the model is applied in the Air Polluter Standard Index (APSI) in Surabaya City, East Java. The results of this study indicate that Fixed Effect GWPR model with a fixed exponential kernel weighting function is the best model to describe the APSI because it has the smallest AIC.

scholarly journals Analisis Regresi Panel pada Pemodelan Tingkat Kematian Bayi di Provinsi Sulawesi Selatan

2020 ◽  
Vol 2 (2) ◽  
pp. 115
Author(s):  
Syafruddin Side ◽  
S. Sukarna ◽  
Raihana Nurfitrah
Keyword(s):  
Panel Data ◽  
Infant Mortality ◽  
Regression Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Least Square ◽  
Dummy Variable ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

Penelitian ini membahas mengenai estimasi parameter model regresi data panel pada pemodelan tingkat kematian bayi di Provinsi Sulawesi Selatan dari tahun 2014 sampai dengan 2015. Data yang digunakan adalah data sekunder dari Dinas Kesehatan Provinsi Sulawesi Selatan yang berupa jumlah kematian bayi, berat bayi lahir rendah, persalinan yang ditolong oleh tenaga kesehatan, penduduk miskin, bayi yang diberi ASI ekslusif dan rumah tangga berperilaku bersih sehat di seluruh Kabupaten/Kota di Provinsi Sulawesi Selatan tahun 2014-2016. Analisis data dilakukan dengan menggunakan penghitungan manual dan dengan menggunakan software EViews 9. Pembahasan dimulai dari melakukan estimasi parameter model regresi data panel, menentukan model regresi data panel terbaik, , menguji asumsi model regresi data panel, pengujian signifikansi parameter dan interpretasi model regresi. Dalam penelitian ini diperoleh kesimpulan yaitu estimasi model regresi data panel terbaik dengan pendekatan fixed effect model.Kata kunci:Regresi Data Panel, Kematian Bayi, Fixed Effect Model, Least Square Dummy Variable. This research discusses about parameter estimation of panel data regression model of infant mortality level modelling in South Sulawesi from 2014 to 2015. The data used were secondary data from Dinas Kesehatan Provinsi Sulawesi Selatan in the form of number of infant mortality, low weight of infant, childbirth rescued by health workers, poor population, infants who were given exclusive breast milk and household that behaves well in the whole district/town in South Sulawesi year 2014-2016. Data analysis was performed using the calculation manually and by using EViews 9 software. The discussion started from doing parameter estimation of panel data regression model, determining the best panel data regression model, testing the assumption of panel data regression model, testing the signification of parameter and interpretation of regression model. Conclusion of this research are the estimation of regression model is the best panel data regression model with fixed effects model approach.Keywords:Panel Data Regression, Infant Mortality, Fixed Effect Model, Least Square Dummy Variable.

scholarly journals MODEL GEOGRAPHICALLY WEIGHTED PANEL REGRESSION (GWPR) DENGAN FUNGSI KERNEL FIXED GAUSSIAN PADA INDEKS PEMBANGUNAN MANUSIA DI JAWA TIMUR

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Dia Cahya Wati ◽  
Herni Utami
Keyword(s):  
Human Development ◽  
Human Development Index ◽  
Weighting Function ◽  
Average Length ◽  
Local Linear Regression ◽  
Coefficient Of Determination ◽  
Model Parameters ◽  
Panel Regression ◽  
Spatial Effects ◽  
Development Index

The Geographically Weighted Panel Regression (GWPR) model is a com-bination of panel data and GWR. The GWPR model is a development of the globalregression model where ideas are taken from non-parametric regression. This model is alinear regression model that is local (local linear regression) which produces an estima-tor of the model parameters that affects local for each point or location where the datais collected. The purpose of this study is form a GWPR model with a fixed gaussiankernel weighting function in overcoming the problem of spatial effects and geographicalfactors that affect an area to another region. The data used in this study is secondarydata taken from the Central Statistics Agency (BPS) website consisting of the HumanDevelopment Index in East Java 2013-2016. This study produces data for the making ofthe Human Development Index using the GWPR method in the formation of the model,where the coefficient of determination generated is 98,74%.Factors that increase HDI es-pecially Mojokerto Regency are average length of school (RLS), life expectancy (AHH),and the construction expensiveness index (IKK). Keywords: GWPR, Fixed Gaussian, Human Development Index, East Java.

scholarly journals ANALISIS REGRESI UNTUK DATA PANEL PADA PEMODELAN TINGKAT KEMATIAN IBU DI PROVINSI SULAWESI SELATAN

2020 ◽  
Vol 2 (1) ◽  
pp. 20
Author(s):  
Muhammad Imran Rahman ◽  
Muhammad Nusrang ◽  
S. Sudarmin
Keyword(s):  
Parameter Estimation ◽  
Panel Data ◽  
Regression Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Least Square ◽  
South Sulawesi ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

Abstrak. Penelitian ini membahas mengenai estimasi parameter model regresi data panel pada pemodelan tingkat kematian ibu di Provinsi Sulawesi Selatan dari tahun 2014 sampai dengan 2016. Data yang digunakan adalah data sekunder dari Dinas Kesehatan Provinsi Sulawesi Selatan yang berupa jumlah kematian ibu, perdarahan, hipertensi dalam kehamilan, infeksi dan gangguan sistem peredaran darah di seluruh Kabupaten/Kota di Provinsi Sulawesi Selatan tahun 2014-2016. Pembahasan dimulai dari melakukan estimasi parameter model regresi data panel, menentukan model regresi data panel terbaik, menguji asumsi model regresi data panel, pengujian signifikansi parameter dan interpretasi model regresi. Dalam penelitian ini diperoleh kesimpulan yaitu estimasi model regresi data panel terbaik dengan pendekatan fixed effect model dengan nilai 𝑅2 = 90%. Adapun peubahpeubah yang berpengaruh signifikan terhadap kematian ibu adalah perdarahan, hipertensi dalam kehamilan dan infeksi. Dari hasil analisis diperoleh juga daerah yang memiliki jumlah kematian ibu terbesar di Provinsi Sulawesi Selatan tahun 2014-2016 adalah Bone dan Jeneponto.Kata Kunci: Regresi data Panel, Angka Kematian Ibu, Fixed Effect Model, Least Square Dummy Variable.Abstract. This research discusses about parameter estimation of panel data regression model of mother mortality level modelling in South Sulawesi from 2014 to 2016. The data used were secondary data from Dinas Kesehatan Provinsi Sulawesi Selatan in the form of number of mother mortality, bleeding, infection, circulatory system disorders and metabolic disorders in the whole district/town in South Sulawesi year 2014-2016. The discussion started from doing parameter estimation of panel data regression model, determining the best panel data regression model, testing the assumption of panel data regression model, testing the signification of parameter and interpretation of regression model. Conclusion of this research are the estimation of regression model is the best panel data regression model with fixed effects model approach with value of 𝑅2 = 90%. The variables that significantly influence maternal mortality are bleeding, hypertension in pregnancy and infection. From the results of the analysis, it was also found that the regions that had the largest number of maternal deaths in South Sulawesi Province in 2014-2016 were Bone and Jeneponto.Keywords: Panel Data Regression, Mother Mortality Rate, Fixed Effect Model, Least Square Dummy Variable.

scholarly journals PENGARUH TINGKAT PENDIDIKAN, INVESTASI, DAN TINGKAT UPAH TERHADAP ANGKATAN KERJA YANG AKTIF BEKERJA DI INDONESIA

Jurnal Ecogen ◽  
2019 ◽  
Vol 1 (3) ◽  
pp. 691
Author(s):  
Vivi Violita Firda ◽  
Syamsul Amar
Keyword(s):  
Secondary Data ◽  
Work Force ◽  
Fixed Effect Model ◽  
Equation Model ◽  
Education Level ◽  
Fixed Effect ◽  
Panel Regression ◽  
Effect Model ◽  
Positive Effect ◽  
Level 2

This study aims to find out and analyze the influence of (1) Education Level, (2) Investment, and (3) Wage Levels on the workforce that is actively working in Indonesia by using the panel regression equation model and using the Fixed Effect Model (FEM) approach. The estimation results show that (1) Education Level has a positive and significant effect on the workforce that is actively working in Indonesia, (2) Investment has a positive and not significant effect on the workforce that is actively working in Indonesia, (3) Wage Levels have a positive and significant effect on workforce that is actively working in Indonesia.This type of research is descriptive and associative. Data type is secondary data. This study uses panel data, which uses 32 provinces in Indonesia using the Fixed Effect Model (FEM) approach.The results of this study indicate that: (1) Education Level has a positive and significant effect on the Active Work Force in Indonesia (2) Investment has a positive effect and has no significant effect on the Active Work Force in Indonesia. (3) Wage Levels have a positive and significant effect on the Active Work Force in Indonesia. Keywords: Labor Force, Education Level, Investment, and Wage Level

scholarly journals The Model of Per-Capita Expenditure Figures in Sumatera Selatan uses a Geographically Weighted Panel Regression

2021 ◽  
Vol 5 (1) ◽  
pp. 61-74
Author(s):  
Dia Cahya Wati ◽  
Dea Alvionita Azka ◽  
Herni Utami
Keyword(s):  
Regression Model ◽  
Weighting Function ◽  
Secondary Data ◽  
Coefficient Of Determination ◽  
Panel Regression ◽  
Spatial Effects ◽  
Capita Expenditure ◽  
Expenditure Rate ◽  
Global Regression ◽  
Per Capita

The Geographically Weighted Panel Regression (GWPR) is a development of a global regression model where the basic idea is taken from a combination of panel data and GWR. The GWPR model is built from the point approach method, which is based on the position of the coordinates of latitude and longitude. The parameters for the regression model at each location will produce different values. GWPR can accommodate spatial effects, so that it can better explain the relationship between response variables and predictors. The purpose of this study is to compare the GWPR model with the Fixed Gaussian and Adaptive Bisquare weighting functions based on the AIC value. The data used in this study is secondary data taken from the website of the Central Statistics Agency (BPS) in the form of Per-Capita Expenditure Figures in South Sumatra in 2013-2019. This research results that in the case of the Per-Capita Expenditure Rate (AP), it is better to use the GWPR method with a fixed gaussian weighting function in the modeling, where the resulting coefficient of determination is 95.81% rather than adaptive bisquare with a determination coefficient of 93.3%. The factors that influence the Per-Capita Expenditure Rate (AP) in South Sumatra on the fixed gaussian weighting are divided into 6 groups, while the adaptive bisquare is divided into 2 groups.

scholarly journals Panel Regression Models for Paddy (Oryza sativa) Crop Production

YMER Digital ◽  
2022 ◽  
Vol 21 (01) ◽  
pp. 28-40
Author(s):  
Rajarathinam A ◽  
◽  
Suba S S ◽  
Keyword(s):  
Regression Model ◽  
Tamil Nadu ◽  
Crop Production ◽  
Wald Test ◽  
Fixed Effect Model ◽  
Panel Regression ◽  
Effect Model ◽  
Tamil Nadu State ◽  
Crop Area ◽  
Paddy Crop

The present investigation was carried out to study area production trends of Paddy crop grown in different districts of Tamil Nadu state, India during the period 1998-99 to 2010- 2020 based on Panel Regression Model. The statistically most suited Panel Regression model was selected based on Hausman and Wald test. The study variables namely the area under the Paddy crop (AREA) and the production (PRODN) of Paddy crop were found to be stationary at level. Analysis of variance test indicated that district to district crop productions were highly significant. Highest area under the crops and productions were registered in Tiruvarur, Thanjavur etc., Very lowest were registered in Coimbatore and Nilgiris districts. The fixed effect model was found to be suitable to study the trend and this model explains the 87% of variations in Paddy crop production.

scholarly journals DEFISIT ANGGARAN PENDAPATAN DAN BELANJA DAERAH KABUPATEN/KOTA PROVINSI DI SUMATERA SELATAN

MBIA ◽  
2021 ◽  
Vol 20 (2) ◽  
pp. 111-122
Author(s):  
Muhammad Bahrul Ulum ◽  
Ayu Geby Gisela Syaputri
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Budget Deficit ◽  
Capital Expenditure ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Coefficient Of Determination ◽  
Local Expenditure ◽  
Effect Model ◽  
Negative Effect

This study aims to see how far the influence of regional own income, capital expenditure, and GDP on the budget deficit of regional own income and local expenditure in 14 districts/cities in South Sumatra province with used panel data with the number of time series from 2010-2019 and analysis method used is panel data regression by using fixed-effect model (FEM). The results of this study are regional own income and local expenditure have a positive effect on the increase in the budget deficit and GDP has a negative effect on the budget deficit in the South Sumatra province, and the magnitude of the influence of variables regional own income, local expenditure, and GDP with the budget deficit has a coefficient of determination of 94.3 percent which means that the variation of these variables in determining the budget deficit is very strong. Keywords: Budget Deficit, PAD, capital expenditure, GDP Abstak Penelitian ini bertujuan untuk melihat sejauh mana pengaruh pendapatan asli daerah, belanja modal dan PDRB terhadap Defisit Anggaran Pendapatan dan Belanja Daerah pada 14 kabupaten/kota di provinsi Sumatera Selatan dengan data yang digunakan adalah data panel dengan jumlah “time series” dari tahun 2010-2019 dan metode analisis yang digunakan adalah regresi data panel dengan menggunakan fixed effect model (FEM). Hasil dari penelitian ini adalah pendapatan asli daerah dan belanja modal memiliki pengaruh yang positif terhadap peningkatan defisit anggaran serta PDRB memiliki pengaruh yang negatif terhadap defisit anggaran di provinsi Sumatera Selatan, serta besaran pengaruh variabel pendapatan asli daerah, belanja modal dan PDRB terhadap defisit anggaran memiliki koefisien determinasi sebesar 94,3 persen yang berarti bahwa variasi variabel ini dalam menentukan defisit anggaran sangat kuat. Kata kunci: Defisit Anggaran, PAD, Belanja Modal, PDRB

scholarly journals PENGARUH TINGKAT PENDIDIKAN, PENGANGGURAN , DAN KEMISKIAN TERHADAP KRIMINALITAS DI INDONESIA

2019 ◽  
Vol 1 (1) ◽  
pp. 21
Author(s):  
Suci Rahmalia ◽  
Ariusni Ariusni ◽  
Mike Triani
Keyword(s):  
Panel Data ◽  
Significant Influence ◽  
Secondary Data ◽  
Fixed Effect Model ◽  
Equation Model ◽  
Fixed Effect ◽  
Regression Equation ◽  
Panel Regression ◽  
Level Of Education ◽  
Effect Model

This study aims to determine and analyze the influence of (1) Level of Education, (2) Unemployment, and (3) Poverty against crime in Indonesia by using the panel regression equation model and using the Fixed Effect Model (FEM) approach. The estimation results show that (1) the level of education has a negative and not significant effect on criminality in Indonesia, (2) unemployment has a negative and significant effect on crime in Indonesia, (3) poverty has a positive and significant influence on crime in Indonesia.This type of research is descriptive and associative. Data type is secondary data. This study uses panel data, which uses 31 provinces in Indonesia using the Fixed Effect Model (FEM) approach.The results of this study indicate that: (1) The level of education has a negative and insignificant influence on crime in Indonesia, (2) Unemployment has a negative and significant effect on crime in Indonesia, (3) Poverty has a positive and significant influence on crime in Indonesia.

scholarly journals Panel Data Modelling for Indian Food Grain Production

2021 ◽  
Author(s):  
RAJARATHINAM ARUNACHALAM ◽  
Subh S S ◽  
Ramji Madhaiyan
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Uttar Pradesh ◽  
Fixed Effect Model ◽  
Panel Regression ◽  
Data Modelling ◽  
Grain Production ◽  
Effect Model ◽  
Food Grain ◽  
Indian Food

Abstract The present investigation was carried out to study the food grain production trends in different states in India based on Panel Regression Model for the period 2001-02 to 2020-2021. The results reveal that between state-to-state food grain production is highly significant the highest food grain production was registered in Uttar Pradesh followed by Punjab and Madhya Pradesh. Very lowest was registered in Kerala and Himachal Pradesh. The findings reveal that the highly significant fixed effect model was found to be suitable to study the trend and this model explains the 82% of variations in food grain production. Over all increasing in food grain production is noted.

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