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

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.

Using information from singletons in fixed-effects estimation: xtfesing

2020 ◽  
Vol 20 (4) ◽  
pp. 965-975
Author(s):  
Laura Magazzini ◽  
Randolph Luca Bruno ◽  
Marco Stampini
Keyword(s):  
Panel Data ◽  
Method Of Moments ◽  
Fixed Effects ◽  
Generalized Method Of Moments ◽  
Panel Data Regression ◽  
Data Regression ◽  
Method Of Moments Estimator ◽  
Fixed Effects Estimation

In this article, we describe the xtfesing command. The command implements a generalized method of moments estimator that allows exploiting singleton information in fixed-effects panel-data regression as in Bruno, Magazzini, and Stampini (2020, Economics Letters 186: Article 108519).

scholarly journals PENINGKATAN TARIF CUKAI ROKOK DAN DAMPAKNYA TERHADAP PEREKONOMIAN DAN PENDAPATAN SEKTORAL JAWA TENGAH

KINERJA ◽  
2017 ◽  
Vol 19 (2) ◽  
pp. 99
Author(s):  
Fatoni Ashar ◽  
Firmansyah ,
Keyword(s):  
Panel Data ◽  
Household Income ◽  
Fixed Effect Model ◽  
Cigarette Consumption ◽  
Input Output ◽  
Output Analysis ◽  
Central Java ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

This study analyzes the effect of excise of cigarette price changes to the consumption of cigarette and Central Java’s economy and household income. In the first stage, with employing panel data regression model,i.e. fixed effect model (FEM) which include 35 regencies/cities in Central Java Province during 2009-2013, the study examines the effect of cigarette excise to cigarette consumption. On the next stage, the study simulatesthe impact of cigarette consumption shock to the Central Java’s sectoral economy and household income using the Central Java 2013 Input-Output table. The findings indicate that the cigarette excise has a tradeoff effect tohousehold’s cigarette consumption. The increase of cigarette excise reduces cigarette consumption, and next, reduces output and sectoral household income. The cigarettes industries suffered the highest impact of thedecrease of the cigarette consumption, followed by other sectors which is has a high link to cigarette industries such as agricultures and tobacco sectors.Keywords: cigarette, excise, panel data regression, input-output analysis

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