scholarly journals Pemodelan Pertumbuhan Ekonomi Provinsi Sulawesi Selatan dengan Menggunakan Regresi Data Panel

2020 ◽  
Vol 2 (3) ◽  
pp. 158
Author(s):  
Misriani Suardin ◽  
Muhammad Nadjib Bustan ◽  
Ansari Saleh Ahmar
Keyword(s):  
Economic Growth ◽  
Panel Data ◽  
Total Population ◽  
Random Effect ◽  
Random Effect Model ◽  
South Sulawesi ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression ◽  
Human Capital Index

Abstract. Economic growth is a process for change the economic condition a country or regional by continuously for the better condition as long as definite period. Economic growth in South Sulawesi for 2013-2016 have up and down because many factors have influence it. Like jobless, human capital index, regional revenue, expenditure, and total population. This research was conducted to determine the factors that influence economic growth in South Sulawesi by using data panel regression methods. Panel data regression is a regression by using panel data. Panel data is a statistics analysis method that combines between time series data and cross section data. The result indicates that the result if the regression analysis on the =5% show that the best panel data regression model is random effect model and human capital index variable have significant effect on economic growth with probability value about 0,0227. Meanwhile, jobless, regional revenue, expenditure, and total population no significant.Keywords: Panel Data Regression, Economic Growth, Common Effect Model, Fixed Effcet Model, Random Effect Model

scholarly journals Analisis Faktor-Faktor yang Mempengaruhi Jumlah Penduduk di Kota Semarang Menggunakan Metode Regresi Data Panel

2019 ◽  
Vol 12 (2) ◽  
pp. 36-41
Author(s):  
Prizka rismawati Arum
Keyword(s):  
Panel Data ◽  
Random Effect ◽  
Geographical Area ◽  
Random Effect Model ◽  
Factors Affecting ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression ◽  
The Relationship ◽  
Number Of Births

Residents are all people who live in the geographical area of Indonesia for six months or more and or those who have been domiciled for less than six months but aim to settle. Population growth is caused by two components, namely: fertility and mortality. To find out how big the relationship between the  population and the number of births and deaths in each sub-district of Semarang, must observed in several specific time periods and places at once. So in this study, the panel data regression method was used. In panel data regression testing, the results show that the panel data regression model formed to determine the factors that influence the level of population is the random effect model. In this model all assumptions are fulfilled. Significant factors affecting population are number of births. Births and deaths affect the population of 99.95% and the remaining 0.05% is influenced by other factors not examined Penduduk adalah semua orang yang berdomisili di wilayah geografis Indonesia selama enam bulan atau lebih dan atau mereka yang berdomisili kurang dari enam bulan tetapi bertujuan menetap. Pertumbuhan penduduk diakibatkan oleh dua komponen yaitu: fertilitas dan mortalitas. Untuk mengetahui seberapa besar keterkaitan antara jumlah penduduk dengan jumlah kelahiran dan kematian di setiap kecamataan Kota Semarang, harus diamati dalam beberapa periode waktu tertentu dan beberapa tempat secara bersamaan. Sehingga dalam penelitian ini digunakan metode regresi data panel. Dalam pengujian regresi data panel, didapatkan hasil bahwa Model regresi data panel yang terbentuk untuk mengetahui faktor-faktor yang mempengaruhi tingkat jumlah penduduk adalah model random Effect. Pada model tersebut semua asumsi terpenuhi. Faktor yang signifikan mempengaruhi jumlah penduduk adalah jumlah kelahiran. Kelahiran dan kematian mempengaruhi jumlah penduduk sebesar 99.95% dan sisanya sebesar 0.05% dipengaruhi oleh faktor- faktor lain yang tidak di teliti.    

scholarly journals THE EFFECT OF SPIN-OFF POLICY AND MACROECONOMIC FUNDAMENTAL FACTORS ON THIRD PARTY FUNDS AT SHARIA GENERAL BANKS

2021 ◽  
Vol 5 (1) ◽  
pp. 08-22
Author(s):  
Fatima Tuzzahara Alkaf ◽  
Nana Nawasiah
Keyword(s):  
Panel Data ◽  
Random Effect ◽  
Random Effect Model ◽  
Third Party ◽  
Financial Report ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression ◽  
Report Data ◽  
The Government

In enhancing the development of Islamic banking, the government issued Law No. 21 of 2008 concerning spin-off. With this policy, it is expected that Islamic Commercial Banks will develop. This study aims to implement panel data regression to examine in depth the influence of spin-off policy and macroeconomic fundamental factors on third party funds of Sharia General Banks. Sampling by purposive sampling, six (6) Sharia General Banks that have conducted spin-offs and financial report data from 2014-2018. The Chow Test and the Hausman Test show that the panel data regression model that matches the variable data used in 2014-2018 is the Random Effect Model (REM). Empirical results show that during the 2014-2018 period, the spin-off policy and macroeconomic fundamental factors had a significant effect on the bank's third-party funds simultaneously. Partially, only the spin-off policy has a significant effect on third party funds.

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 Pengaruh inflasi dan laju pertumbuhan penduduk terhadap kemiskinan di Sumatera Bagian Selatan tahun 2011-2018

SOROT ◽  
2020 ◽  
Vol 15 (1) ◽  
pp. 33
Author(s):  
Kevin Kevin ◽  
Aning Kesuma Putri ◽  
Aja Nasrun
Keyword(s):  
Growth Rate ◽  
Panel Data ◽  
Population Growth ◽  
Population Growth Rate ◽  
Random Effect ◽  
Random Effect Model ◽  
Effect Model ◽  
Panel Data Regression ◽  
Analysis Technique ◽  
Data Regression

Penelitian ini bertujuan untuk mengetahui pengaruh inflasi dan laju pertumbuhan penduduk terhadap kemiskinan di Sumatera bagian Selatan (Sumbagsel) tahun 2011-2018. Penelitian ini menggunakan pendekatan kuantitatif. Data yang digunakan adalah data panel ibu kota provinsi di Sumbagsel yang terdiri dari Palembang, Pangkalpinang, Bandar Lampung, Bengkulu, dan Jambi tahun 2011-2018. Teknik analisis data dalam penelitian ini menggunakan analisis regresi data panel dengan model Random Effect. Hasil penelitian menunjukkan bahwa secara simultan variabel inflasi dan laju pertumbuhan penduduk berpengaruh signifikan terhadap kemiskinan. Secara parsial variabel inflasi tidak berpengaruh signifikan terhadap kemiskinan sedangkan variabel laju pertumbuhan penduduk berpengaruh signifikan terhadap kemiskinan.This research aimed to find out the effect of inflation and population growth rate on the poverty in Southern Sumatera in 2011-2018. This research used the quantitative approach. The data used is panel data of the capital of province in Southern Sumatera which consists of Palembang, Pangkalpinang, Bandar Lampung, Bengkulu, dan Jambi in 2011-2018. The data analysis technique was the panel data regression analysis using random effect model. The result of the research showed that simultaneously the variable of inflation and population growth rate have a significant effect on the poverty. Partially the variable of inflation have no significant effect on the poverty while the variable of population growth rate has a significant effect on the poverty.

scholarly journals Regresi Data Panel dengan Pendekatan Common Effect Model (CEM), Fixed Effect model (FEM) dan Random Effect Model (REM) (Studi Kasus: Persentase Penduduk Miskin Menurut Kabupaten/Kota di Kalimantan Timur Tahun 2015-2018)

2020 ◽  
Vol 1 (2) ◽  
pp. 106
Author(s):  
Eka Nur Amaliah ◽  
Darnah Darnah ◽  
Sifriyani Sifriyani
Keyword(s):  
Panel Data ◽  
Random Effect ◽  
Random Effect Model ◽  
Series Data ◽  
Poor People ◽  
Fem Model ◽  
East Kalimantan ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

Panel data regression is a regression that combines cross section data and time series data. Panel data regression estimation can be done through 3 estimates namely CEM, FEM and REM. This research will make a modeling of the percentage of poor people according to regencies / cities in East Kalimantan using panel data regression analysis. Poverty occurs due to lack of income and assets to meet basic needs. For this reason, variables that are assumed to affect the percentage of the poor are used, including the Population Growth Rate (LPP), Human Development Index (HDI), and Adjustable Per capita Expenditure (PPD). By using 3 CEM, FEM and REM approaches based on testing, the best FEM model is obtained. Based on the FEM model the factors that significantly influence are the HDI and PPD. A value of 0.7755 means that the HDI and PPD can explain the percentage of poor people according to the Regency / City in East Kalimantan of 77.55% while the remaining 22.45% is influenced by other variables not yet included in the model.

scholarly journals Effect of Special Allocation Funds (DAK), Capital Expenditures, and Investment on Economic Growth in the Sulawesi Region

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Fera Nur Ariyani Ibrahim ◽  
Irawati Abdul
Keyword(s):  
Economic Growth ◽  
Significant Influence ◽  
Capital Expenditure ◽  
Random Effect ◽  
Random Effect Model ◽  
Secondary Data ◽  
Capital Expenditures ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

This study aims to analyze the effect of Special Allocation Funds, Capital Expenditures and Investment on Economic growth in the Sulawesi region. The data used are secondary data obtained from Simreg Bappenas and the Central Statistics Agency during 2007-2016. The unit of analysis is 6 provinces in the Sulawesi region. This study uses a panel data regression model with the Random Effect Model (REM) method. The results of this study indicate that, (i) the Special Allocation Fund has a negative and significant effect on economic growth in the Sulawesi region. (ii) Capital expenditure shows a positive and significant influence on economic growth in the Sulawesi region. (iii) Investment shows a positive and significant influence on economic growth in the Sulawesi region.Keywords: Special Allocation Funds, Capital Expenditures, Investment, Economic Growth, Random Effect Model (REM).

scholarly journals KETIMPANGAN PEMBANGUNAN EKONOMI REGIONAL KALIMANTAN

2020 ◽  
Vol 3 (1) ◽  
pp. 22-25
Author(s):  
Mutia Madina ◽  
Muzdalifah M
Keyword(s):  
Economic Development ◽  
Panel Data ◽  
Quantitative Research ◽  
Random Effect ◽  
Random Effect Model ◽  
Secondary Data ◽  
Research Approach ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

Abstrack- This research was conducted to (1) Know the economic development in  Kalimantan region from 2011 to 2018; (2) Analyze the effect of regional share, proportional shift, and competitive shift on inequality in economic development. The data analysis technique used are: the first, Klassen typology analysis as a descriptive approach, and the second, the panel data regression analysis with random effect model estimation as a quantitative research approach. This research uses secondary data. Klassen's typological analysis showed the results that not all the provinces have positive growth every year. The analysis of panel data regression showed the results that the regional share and the competitive shift do not affect the development disparities which occurred in Kalimantan. Meanwhile, the proportional shifts affect the imbalance of development which occurred in Kalimantan region. Keywords: Economic Development Inequality, Klassen Typology, Regional Share, Proportional Shift, Competitive Shift. Abstrak- Penelitian ini dilakukan untuk (1) Mengetahui perkembangan perekonomian region kalimantan mulai tahun 2011 sampai 2018; (2) Menganalisis pengaruh regional share, proportional shift, dan competitive shift terhadap ketimpangan pembangunan ekonomi.Teknik analisa data yang digunakan yaitu pertama analisis tipologi klassen sebagai pendekatan deskriptif dan yang kedua yaitu analisis regresi data panel dengan estimasi random effect model sebagai pendekatan penelitian kuantitatif. Penelitian ini menggunakan data sekunder.Analisis tipologi klassen menunjukkan hasil bahwa tidak semua provinsi memiliki pertumbuhan positif setiap tahunnya. Analisis regresi data panel menunjukkan hasil bahwa regional share dan competitive shift tidak mempengaruhi ketimpangan pembangunan yang terjadi di regional Kalimantan. Sedangkan proportional shift mempengaruhi ketimpangan pembangunan yang terjadi di regional Kalimantan. Kata kunci : Ketimpangan Pembangunan Ekonomi, Tipologi Klassen, Regional Share, Proportional Shift, Competitive Shift.

Tingkat Kemiskinan di Sumatra Selatan dan Analisisnya

2020 ◽  
Vol 8 (2) ◽  
pp. 127-133
Author(s):  
Doni Putra ◽  
Rifki Khoirudin
Keyword(s):  
Research Method ◽  
Random Effect ◽  
Random Effect Model ◽  
Poverty Rate ◽  
Variable Number ◽  
Regression Method ◽  
Poverty Level ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression

This study aims to determine the factors that affect the poverty rate of regencies / cities in South Sumatra Province in 2011 to 2017. In this study the factors that affect poverty rates are related to unemployment, HDI, MSE, and population. The research method used is the panel data regression method using the help of Eviews software. The final thanks is the Random Effect Model. The results of this study are the variable Number of Population has a significant effect on the level of poverty in the District / City in South Sumatra Province. However, the Unemployment Rate Variable, HDI, and UMK were not significant to the poverty level in the regencies / cities in South Sumatra Province.

scholarly journals Analysis Of Effect Of ZIS (Zakat, Infaq, And Shadaqah), Regional Domestic Products Of Bruto, Regional Minimum Wage And Inflation On Levels Poverty In Indonesia 2012 – 2016

2019 ◽  
Vol 2 (2) ◽  
pp. 193-211
Author(s):  
Fiky Nila Mustika ◽  
Eni Setyowati ◽  
Azhar Alam
Keyword(s):  
Panel Data ◽  
Random Effect ◽  
Random Effect Model ◽  
Secondary Data ◽  
Minimum Wages ◽  
Effect Model ◽  
Panel Data Regression ◽  
The Impact ◽  
Poverty Levels ◽  
Domestic Products

This study investigated the impact of ZIS (Zakat, Infaq, and Sadaqah) Gross Regional Domestic Products, Regional Minimum Wages, and Inflation on Poverty Levels in Indonesia during the 2012-2016 period. .This paper used secondary data in the panel data form. This research conducted a quantitative approach using panel data regression. Based on the results of the panel data testing, the best model chosen is the Random Effect Model (REM). Variables of gross regional domestic products and regional minimum wages have a significant effect on poverty levels in Indonesia while the variables of zakat, infaq, and shadaqah (ZIS) and inflation do not influence the level of poverty in Indonesia.

scholarly journals DIVERSIFIKASI EKSPOR NON MIGAS INDONESIA KE PASAR NON TRADISIONAL

2020 ◽  
Vol 14 (2) ◽  
pp. 215-238
Author(s):  
Hotsawadi Harahap ◽  
Widyastutik
Keyword(s):  
United Arab Emirates ◽  
Oil And Gas ◽  
Random Effect ◽  
Random Effect Model ◽  
Structural Match ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression ◽  
Oil Exports

Abstrak Penelitian ini bertujuan untuk menganalisis diversifikasi ekspor non migas Indonesia ke pasar non tradisional. Metode penelitian yang digunakan adalah analisis statistik deskriptif dengan pendekatan pengelompokan (clustering), Structural Match Index dan Demand Index, serta regresi data panel. Hasil penelitian menunjukkan bahwa negara yang diidentifikasikan sebagai negara non tradisional potensial adalah Brazil, Pantai Gading, Mesir, Georgia, Jamaica, Kazakhstan, Kuwait, Myanmar, Nigeria, Norway, Oman, Pakistan, Russian Federation, Trinidad and Tobago, Turkey, United Arab Emirates, dan Uruguay. Hasil regresi data panel menunjukkan bahwa Random Effect Model merupakan model yang terbaik untuk menjelaskan faktor-faktor yang memengaruhi ekspor non migas Indonesia ke negara non tradisional. Hasil regresi menunjukkan bahwa GDP riil negara tujuan, populasi negara tujuan, nilai tukar riil, FDI dan kualitas pelabuhan Indonesia berpengaruh signifikan secara statistik terhadap ekspor non migas Indonesia ke negara non tradisional potensial tersebut. Beberapa rekomendasi kebijakan yang perlu dilakukan untuk meningkatkan ekspor non migas ke negara tujuan non tradisional diantaranya perlu dilakukan intelejen pasar mengenai kebutuhan dan selera dari masing-masing negara non tradisional atas produk Indonesia, peningkatan kualitas pelabuhan Indonesia dan kebijakan tambahan yang memberikan insentif untuk menarik Foreign Direct Investment ke Indonesia. Kata Kunci: Diversifikasi Ekspor, Demand Index, Non traditional, Random Effect Model, Structural Match Index   Abstract This study aims to analyze the diversification of Indonesia's non-oil and gas exports to non-traditional markets. The research method used is descriptive statistical analysis with a clustering approach, Structural Match Index and demand index, and panel data regression. The results showed that countries identified as potential non-traditional countries were Brazil, Ivory Coast, Egypt, Georgia, Jamaica, Kazakhstan, Kuwait, Myanmar, Nigeria, Norway, Oman, Pakistan, Russian Federation, Trinidad and Tobago, Turkey, United Arab Emirates, and Uruguay. The panel data regression results show that the random effect model is the best model to explain the factors that influence Indonesia's non-oil exports to non-traditional countries. The results show that the real GDP of the destination country, the population of the destination country, the real exchange rate, FDI and the quality of Indonesia's ports have a statistically significant effect on Indonesia's non-oil exports to these potential non-traditional countries. Then, in this study there are several policy recommendations that need to be done to increase non-oil and gas exports to non-traditional destination countries including market intelligence regarding the needs and tastes of each non-traditional country for Indonesian products, improving the quality of Indonesian ports and additional policies that provide incentives to attract Foreign Direct Investment to Indonesia. Keywords:  Export Diversification, Demand Index, Non-traditional, Random Effect Model, Structural Match Index JEL Classifications: F13, F15, F18

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