scholarly journals Asymptotic Properties of the Disturbance Variance Estimator in a Spatial Panel Data Regression Model with a Measurement Error Component

2010 ◽  
Vol 17 (3) ◽  
pp. 349-356
Author(s):  
Jae-Jun Lee
Keyword(s):  
Measurement Error ◽  
Panel Data ◽  
Regression Model ◽  
Asymptotic Properties ◽  
Error Component ◽  
Variance Estimator ◽  
Spatial Panel ◽  
Panel Data Regression ◽  
Data Regression ◽  
Spatial Panel Data

scholarly journals Modeling Human Development Index of Bali with Spatial Panel Data Regression

2019 ◽  
Vol 4 (5) ◽  
pp. 132-137
Author(s):  
Mita Lasdiyanti ◽  
Eka N. Kencana ◽  
Putu Suciptawati
Keyword(s):  
Panel Data ◽  
Human Development ◽  
Human Development Index ◽  
Panel Data Analysis ◽  
Spatial Error ◽  
Development Index ◽  
Spatial Panel ◽  
Panel Data Regression ◽  
Data Regression ◽  
Spatial Panel Data

Human development index (HDI) is an index that represents the successfulness of human development in a region. For Bali, one of 34 provinces in Indonesia, the progress of HDI in the period 2010–2017 showed an increasing trend. In the year 2010, the Bali’s HDI is accounted for 70.10, gradually increase to 74.30 in the year 2017. However, in 2017 there are some regions with their HDIs are below of Bali’s HDI, namely Jembrana, Buleleng, Klungkung, Bangli, and Karangasem. The aim of this work is to model the HDI of 9 regencies of Bali so that the main determinant to increase the HDIs especially for the regencies with lower HDIs could be determined. The model consists of one dependent variable (HDI) with three indicators as the independent ones, there are (a) life expectancy, (b) education, and (b) standard of living. By applying spatial panel data analysis, five models were built i.e. CEM, FEM (individual), FEM (time), REM, and spatial error FEM to determine the effect of each indicator. The result shows the best model is spatial error FEM in which education has the biggest influence compare than the others.

scholarly journals MEMODELKAN TINGKAT PENGANGGURAN TERBUKA DI INDONESIA

2021 ◽  
Vol 10 (1) ◽  
pp. 20
Author(s):  
KADEK YUSA MAHENDRA ◽  
MADE SUSILAWATI ◽  
NI LUH PUTU SUCIPTAWATI
Keyword(s):  
Panel Data ◽  
Unemployment Rate ◽  
Fixed Time ◽  
Spatial Panel ◽  
Panel Data Regression ◽  
Data Regression ◽  
Aceh Province ◽  
Head Count ◽  
Spatial Panel Data ◽  
Significant Factors

Unemployment is one of the problems in the field in economic development. To determine the development of open unemployment in a region, an indicator of the open unemployment rate is used. The highest of open unemployment rate is Aceh Province and Maluku Province in 2015 at 9,93 percent and the lowest Bali Province in 2018 at 1,37 percent. The purpose of this work is to modeling and determine the significant factors that affect the open unemployment rate in Indonesia by applying spatial panel data regression. The results show indicate that there is no effect of spatial dependence on the model, so the model chosen to model the open unemployment rate in Indonesia is a panel data model with a fixed time effect and significant affect by head count index, the percentage of illiterate people, the provincial minimum wage, and the number of the workforce.

scholarly journals PEMODELAN DATA KEMISKINAN PROVINSI JAWA TENGAH MENGGUNAKAN FIXED EFFECT SPATIAL DURBIN MODEL

2019 ◽  
Vol 8 (2) ◽  
pp. 220-232
Author(s):  
Siska Alvitiani ◽  
Hasbi Yasin ◽  
Mochammad Abdul Mukid
Keyword(s):  
Panel Data ◽  
Fixed Effects ◽  
Spatial Durbin Model ◽  
Spatial Panel ◽  
Central Java ◽  
Panel Data Regression ◽  
Data Regression ◽  
Spatial Panel Data ◽  
Independent Variable ◽  
Poor Population

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

Determinants Of Capital Structure For Multinational And Domestic Companies In India: Panel Data Regression Model

2019 ◽  
Vol 118 (7) ◽  
pp. 147-154
Author(s):  
K. Maheswari ◽  
Dr. J. Gayathri ◽  
Dr. M. Babu ◽  
Dr.G. Indhumathi
Keyword(s):  
Panel Data ◽  
Capital Structure ◽  
Regression Model ◽  
Business Risk ◽  
Return On Assets ◽  
Panel Data Regression ◽  
Data Regression ◽  
Return On Capital ◽  
Domestic Companies ◽  
The Capital Structure

The capital structure refers to the components of capital needed to establish and expand its business activities. The study was made with an objective to examine the determinants of capital structure of multinational and domestic companies listed in S&P BSE automobile sector. The study concluded that there is significant impact on capital structure determinants such as size, business risk, non debt shield tax, return on assets, tangibility, profit, return on capital employed and liquidity on the capital structure of multinational and domestic companies of Indian Automobile Sector.  

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 DETERMINAN INDEKS KEBAHAGIAAN DI INDONESIA

2021 ◽  
Vol 7 (2) ◽  
pp. 34
Author(s):  
Imawan Azhar Ben Atasoge
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Gini Index ◽  
Well Being ◽  
Panel Data Regression ◽  
Data Regression ◽  
Per Capita ◽  
Subjective States

ABSTRAK  Tolok ukur untuk melihat kemakmuran sebuah Negara dapat dilihat dari GDP yang ada di negara tersebut. Ukuran kesejahteraan tidak hanya diukur berdasarkan substansi akan tetapi diukur berdasarkan keadaan subjektif atau kebahagiaan. Tujuan dari penelitian ini ialah mengetahui faktor yang mempengaruhi kebahagiaan di Indonesia periode 2014 dan 2017. Analisis yang digunakan yaitu model regresi data panel. Penelitian ini menunjukkan bahwa variabel pendidikan, kesehatan, indeks gini serta zis berpengaruh secara terhadap kebahagiaan di Indonesia. Sedangkan variabel PDRB per kapita, kemiskinan, dan Indeks Demokrasi. Kata Kunci: Indeks Kebahagiaan, IPM, Kemiskinan, Indeks Gini, Zakat, Indeks Demokrasi ABSTRACTThe benchmark for seeing the prosperity of a country can be seen from the GDP in that country. The measure of well-being is not only measured based on substance but is measured based on subjective states or happiness. The purpose of this study is to determine the factors that influence happiness in Indonesia for the period 2014 and 2017. The analysis used is a panel data regression model. This study shows that the variables of education, health, Gini index and zis have a significant effect on happiness in Indonesia. While the variables are per capita GRDP, poverty, and the Democracy Index.Keywords: Happiness Index, HDI, Poverty, Gini Index, Zakat, Democracy Index

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