scholarly journals PENGARUH TINDAK KORUPSI TERHADAP KEMISKINAN DI NEGARA-NEGARA ASIA TENGGARA DENGAN MODEL PANEL DATA

2020 ◽  
Vol 4 (2) ◽  
pp. 311-320
Author(s):  
Aditya Firman Baktiar ◽  
Herpanindra Fadhilah ◽  
Margareth Dwiyanti Simatupang ◽  
Mula Warman ◽  
Salsa Vira ◽  
...  
Keyword(s):  
Panel Data ◽  
Southeast Asia ◽  
Secondary Data ◽  
Fixed Effect Model ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression ◽  
The World ◽  
Data Source ◽  
High Level

Poverty is still being an issue all over the world. It also happens in Southeast Asia that mostly consists of developing countries that identic with high poverty rates. Countries in the world have tried to eradicate the problem of poverty, it's just that it can be hampered due to the high level of corruption. This study aims to look at suitable models and the relationship between corruption and poverty. The data source in this study is secondary data from ten countries in Southeast Asia from 2015 to 2018. Analysis of the data used in this study is panel data. The result obtained is a panel data regression model that is more suitable for modeling the effect of corruption on poverty in Southeast Asian countries is a fixed effect model. Based on the model, the corruption represented by Corruption Perception Index (CPI) and the poverty represented by Human Development Index (HDI) is directly proportional which means every increase in one unit of CPI will also increase the HDI score by 0.001443 unit.

Dana pihak ketiga, Inflasi dan Pembiayaan Mudharabah terhadap Non Performing Financing pada Bank Islam di Indonesia dan Malaysia

2019 ◽  
Vol 16 (2) ◽  
pp. 74-80
Author(s):  
Afrillia Tiara Putri ◽  
Saadah Yuliana ◽  
Anna Yulianita
Keyword(s):  
Panel Data ◽  
Secondary Data ◽  
Fixed Effect Model ◽  
Third Party ◽  
Coefficient Of Determination ◽  
Islamic Banks ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression ◽  
Negative Effect

This study aimed to analyze the influence of third party funds, inflation, and mudharabah against non performing financing on Islamic Banks in Indonesia and Malaysia. Data used is secondary data. The method used in this analysis is the panel data regression. The results showed that in partial third party fund and mudharabah significant negative effect on the Non Performing Financing, while inflation is positive and not significant to the Non Performing Financing. Variable Third Party Funds, Inflation and mudharabah jointly significant effect on Non Performing Financing. Based on the regression equation fixed effect model results show the results of the coefficient of determination (R2) is 0.369198, or 36.91 per cent means that the variation of the variable third party funds, inflation and mudharabah have an influence on the non performing financing for the coefficient of determination, while the rest 63.09 percent influenced by variables outside the model

scholarly journals PERTUMBUHAN EKONOMI, DISPARITAS PENDAPATAN DAN KESEJAHTERAAN KABUPATEN/KOTA DI PROVINSI NUSA TENGGARA BARAT

GANEC SWARA ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 59
Author(s):  
BAIQ HIPZIWATY ◽  
PUTU KARISMAWAN ◽  
BAIQ ISMIWATY
Keyword(s):  
Economic Growth ◽  
Regression Analysis ◽  
Panel Data ◽  
Secondary Data ◽  
Fixed Effect Model ◽  
Income Disparity ◽  
The People ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

This study aims to analyze economic growth, income disparity and community welfare in the West Nusa Tenggara Province.     This research is a descriptive study using secondary data obtained from relevant agencies in the form of GRDP data, population, economic growth, HDI and per capita income between regencies / cities in NTB Province and data collection using the case method. With analytical procedures using Williamson index and panel data regression analysis.     The results showed that during the period of 2010-2016 the average economic growth of West Nusa Tenggara Province was 6.0%. The income disparity seen from the Williamson index in the 2010-2016 period is classified as a medium inequality criterion. The estimation results of the relationship between the variables of economic growth, income disparity and the welfare of the people of West Nusa Tenggara Province measured using HDI in 2010-2016 using panel data regression analysis with the Fixed Effect model (FEM), found that economic growth variables were positively related, but not significant to welfare society. The variable income disparity is significantly related to the welfare of the people of NTB Province.

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 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

scholarly journals Indonesian Clove Competitiveness and Competitor Countries in International Market

2020 ◽  
Vol 9 (1) ◽  
pp. 39-54
Author(s):  
Adnan Putra Pratama ◽  
Dwidjono Hadi Darwanto ◽  
Masyhuri Masyhuri
Keyword(s):  
Panel Data ◽  
Sri Lanka ◽  
Market Share ◽  
Secondary Data ◽  
International Market ◽  
Panel Data Regression ◽  
Data Regression ◽  
The World ◽  
The Government ◽  
Star Position

Trade liberalization is currently demanding every country to increase the competitiveness of its products. Indonesia as the largest clove producer in the world has a major competitor in the international market. This study aims to determine the competitiveness of Indonesia's clove exports and competing countries in the international market and determine the factors that affect its competitiveness. The data used in this study are secondary data from five major producing countries namely Indonesia, Madagascar, Tanzania, Sri Lanka, and Comoros during the period 2000-2017 sourced from UNComtrade, FAO and the World Bank. Competitiveness is measured by Revealed Comparative Advantage (RCA), Acceleration Ratio (AR) and Export Product Dynamic (EPD) while the factors that affect competitiveness are used panel data regression methods using E-Views software. The results showed that Indonesia had the lowest RCA index, the AR value showed Madagascar and Tanzania were able to capture market share in the international market and the EPD value showed that all countries occupied the rising star position except Sri Lanka in the falling star position. Panel data regression analysis results show that the market share and GDP variables significantly influence the competitiveness of the main clove producing countries while the production variables and export prices do not significantly influence the country's competitiveness. The government must dare to take policies to limit clove imports and increase exports.

scholarly journals THE EFFECT OF MINIMUM WAGE, INVESTMENT, GRDP, AND TECHNOLOGY INDEX ON INDONESIAN LABOR ABSORPTION

2021 ◽  
Vol 6 (1) ◽  
pp. 82
Author(s):  
Siti Safi'atul Ummah
Keyword(s):  
Minimum Wage ◽  
Development Process ◽  
Hypothesis Test ◽  
Secondary Data ◽  
Fixed Effect Model ◽  
Analysis Techniques ◽  
Independent Variables ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

Labor issues become an obstacle to the development process in a country. This problem arises due to a lack of employment opportunities, so that the existing workforce is not maximally absorbed. This problem is not spared from several development factors including the minimum wage, investment, GRDP and technology index. With the aim of knowing the influence of the minimum wage, investment, GRDP and technology index variables with the Indonesian labor absorption variable in 2015-2019. Using secondary data obtained from BPS Indonesia and using panel data regression analysis techniques with the Fixed Effect model as the selected model and using classical assumption tests and hypothesis testing. The results of the hypothesis test show that the influence of the minimum wage, investment, GRDP and technology index variables has an effect on the labor absorption variable simultaneously. The magnitude of the effect (R²) by all independent variables shows that the minimum wage, investment, GRDP and technology index have an effect of 99.82% on the depnden variable (labor absorption).

scholarly journals MODEL REGRESI DATA PANEL UNTUK MENGETAHUI FAKTOR YANG MEMPENGARUHI TINGKAT KEMISKINAN DI PULAU MADURA

2020 ◽  
Vol 9 (3) ◽  
pp. 355-363
Author(s):  
Artanti Indrasetianingsih ◽  
Tutik Khalimatul Wasik
Keyword(s):  
Panel Data ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Series Data ◽  
Poverty Level ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

Poverty arises when a person or group of people is unable to meet the level of economic prosperity which is considered a minimum requirement of a certain standard of living or poverty is understood as a state of lack of money and goods to ensure survival. Panel data regression is the development of regression analysis which is a combination of time series data and cross section data. Panel data regression is usually used to make observations of data that is examined continuously for several periods. The purpose of this study is to determine the factors that influence the level of poverty in Madura Island in the period 2008 - 2017. In this study the variables used in this study are life expectancy (X1), average length of school (X2), level open unemployment (X3), and labor force participation (X4) with the Comman Effect Model (CEM) approach, Fixed Effect Model and Random Effect Model (REM). To choose the best model from the three is the chow test, the hausman test and the breusch-pagan test. In this study, the best model chosen was the Fixed Effect Model. Keywords: CEM, Fixed Effect Model, Data Panel Regression, REM, Poverty level.

scholarly journals ANALISIS FAKTOR YANG MEMPENGARUHI RETURN SAHAM PADA BANK UMUM YANG TERDAFTAR DI BURSA EFEK INDONESIA

2021 ◽  
Vol 5 (1) ◽  
pp. 42-55
Author(s):  
Mulia Andirfa ◽  
Eka Chyntia ◽  
Iva Septarina ◽  
Maryana
Keyword(s):  
Stock Returns ◽  
Commercial Banks ◽  
Stock Exchange ◽  
Secondary Data ◽  
Fixed Effect Model ◽  
Financial Reports ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression ◽  
Positive Effect

This study aims to analyze the effect of ROE, CAR, NPL, BOPO, and DER simultaneously on stock returns in  commercial banks listed on the Indonesia Stock Exchange. The data used in this study are secondary data in the form of financial reports at PT. Bank Rakyat Indonesi Tbk, PT. Bank Negara Indonesia Tbk, PT. Bank Mandiri Tbk, PT. Bank Central Asia Tbk, and PT. Bank Mega Tbk. from 2014-2019. The data analysis method used is panel data regression analysis, namely the Fixed Effect Model (FEM). The results showed that: ROE theoretically and statistically affect stock returns in commercial banks listed on the Indonesia Stock Exchange. CAR is theoretically and statistically insignificant to stock returns in Commercial Banks listed on the Indonesia Stock Exchange. BOPO has a theoretical effect but does not have a statistical and significant effect on stock returns in  commercial banks listed on the Indonesia Stock Exchange. NPL and DER have no effect on stock returns in Commercial Banks listed on the Indonesia Stock Exchange. ROE, CAR, NPL, BOPO and DER simultaneously have a positive effect on stock returns in) Commercial Banks listed on the Indonesia Stock Exchange. ROE, CAR, NPL, BOPO and DER have the ability to explain their effect on stock returns in Commercial Banks listed on the Indonesia Stock Exchange of 44.09%. The remaining 55.01% is influenced by other variables outside this research model.

scholarly journals APLIKASI REGRESI DATA PANEL DENGAN PENDEKATAN FIXED EFFECT MODEL (STUDI KASUS: PT PLN GIANYAR)

2014 ◽  
Vol 3 (1) ◽  
pp. 1 ◽  
Author(s):  
NI PUTU ANIK MAS RATNASARI ◽  
I PUTU EKA NILA KENCANA ◽  
G.K. GANDHIADI
Keyword(s):  
Panel Data ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Least Square ◽  
Dummy Variable ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

Panel data regression has three approaches. One of these approaches is Fixed Effect Model (FEM). FEM is common estimated using Least Square Dummy Variable. The use of dummy variable in FEM is based on assumption that slope coefficients are constant but intercept varies over individuals. One of application of FEM is to find out motivation of employees at PT PLN Gianyar for non-outsourcing and outsourcing employees based on existence, relatedness, and growth. This research yields the following two models:with 67% motivation non-outsourcing employees represented by existenceand73% motivation non-outsourcing employees represented by existence and growth.

scholarly journals PENERAPAN REGRESI DATA PANEL PADA ANALISIS PENGARUH INFRASTRUKTUR TERHADAP PRODUKTIFITAS EKONOMI PROVINSI-PROVINSI DI LUAR PULAU JAWA TAHUN 2010-2014

2018 ◽  
Vol 11 (1) ◽  
pp. 1-15
Author(s):  
Yosephine Magdalena Sitorus ◽  
Lia Yuliana
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Seemingly Unrelated Regression ◽  
Total Output ◽  
Economic Productivity ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

There is inequality between the economic growth of provinces in Java and outside of Java. The total area of Java  is only 6,77% from total area of Indonesia but the Growth Domestic Product (GDP) based on constant price in 2014, Java contributed 57,8% of the GDP total Indonesia. One cause that made this disparity is the development of infrastructure in outside Java is still weak. The development of infrastructure is a basic element for increasing total output production that later will increase the economic growth. However, there are so many problems that occur in developing the infrastructure in outside of Java. This study aimed to analyze the condition of infrastructure provinces outside Java in 2010-2014. The data used is the secondary data for 27 provinces outside of Java 2010-2014 from BPS. The analytical method used is panel data regression with fixed effect model and Seemingly Unrelated Regression (SUR) Model. Based on the results, the infrastructure that affects economic productivity significantly and positively is road infrastructure, health, and budget. Infrastructure that affects economic productivity significantly and negatively is the educational infrastructure. Water and electricity infrastructure did not significantly affect economic productivity.Keywords: Infrastructure, Economic productivity, Panel Data Regression, Fixed Effect Model

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