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 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.

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 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 Is MGNREG Scheme Complementary to Fishing Activities?

2016 ◽  
Vol 5 (1) ◽  
pp. 47-53
Author(s):  
Chiranjib Neogi ◽  
Kamal Ray ◽  
Ramesh Chandra Das
Keyword(s):  
Freshwater Fish ◽  
Rainwater Harvesting ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Series Data ◽  
Component Part ◽  
Indian States ◽  
Effect Model

Freshwater fish output is taken as a proxy variable for empirical assessment of indirect benefits in terms of enhanced quantity of freshwater fish (output) cultivation. It is not unlikely to assess empirically the productivity of subsidized public scheme when rural development or rural asset generations are underlined in the said scheme, MGNREG Act, 2005. Rainwater harvesting is a major component part of the scheme since about 49.5 per cent of the total fund is already utilized on water conservation and obviously it has an impact on the cultivation of freshwater fish output. Time series data on annual expenditure on MGNREG and corresponding freshwater fish output at the state level are taken during the period 2006-07 to 2013-14 for 16 major Indian states. Fixed effect model and random effect models are being applied and the Hausman specification test suggests that fixed effect model is more appropriate than random effect model. Significant differences among the intercepts of the selected states are revealed as per F test. The results of fixed effect panel regression establish that fish output is enhanced by 0.000257 thousand tones or 0.26 tones if MGNREG expenditure rises by one crore or 10 million rupees. 

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.

Estimasi Fixed Effect Model dari Likuiditas Aset, Financial Leverage dan Likuiditas Saham di Indonesia

2019 ◽  
Vol 4 (3) ◽  
pp. 412
Author(s):  
Irdha Yusra ◽  
Awidi Mulfita
Keyword(s):  
Panel Data ◽  
Random Effect ◽  
Random Effect Model ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Financial Leverage ◽  
Stock Liquidity ◽  
Common Effect ◽  
Effect Model ◽  
Asset Liquidity

<p><em>In investing, investors don’t assess the expected return, but also liquidity in shares. Because the aspect of liquidity is very important for investors to decide which stocks are attractive investments. This study aims to examine the effect of asset liquidity and financial leverage on stock liquidity. The population is all companies which are listed in Indonesia Stock Exchange in 2013-2017 periods. The sampling technique uses a purposive sampling method with predetermined criteria and obtained a sample of 58 companies with 290 observations. The data of the financial statement of the companies has been obtained from the official website of IDX. The analytical method used is regression analysis of panel data with the help of application E-Views 8. Panel data regression can be estimated using three models, namely Common Effect Model (CEM), Fixed Effect Model (FEM), and Random Effect Model (REM). From the results of the estimation model, it is found that FEM is the best model in this study. Furthermore, the results of the study show that asset liquidity has a positive and not significant effect on stock liquidity, while financial leverage has a negative and significant effect on stock liquidity.</em></p><p>Dalam berinvestasi, investor tidak hanya menilai dari return yang diharapkan, namun juga likuiditas pada saham. Karena aspek likuiditas sangat penting bagi investor untuk memutuskan mana saham yang menarik investasi. Penelitian ini bertujuan untuk menguji pengaruh likuiditas aset dan financial leverage terhadap likuiditas saham. Populasi dalam penelitian ini adalah perusahaan yang terdaftar di Bursa Efek Indonesia (BEI) periode 2013-2017. Teknik pengambilan sampel menggunakan metode purposive sampling dengan kriteria yang telah ditentukan dan diperoleh sampel sebanyak 58 perusahaan. Data laporan keuangan diperoleh dari website resmi BEI. Metode analisis yang dipakai adalah analisis regresi data panel dengan bantuan aplikasi E-Views 8. Regresi data panel dapat diestimasi menggunakan tiga model, yaitu Common Effect Model (CEM), Fixed Effect Model (FEM), dan Random Effect Model (REM). Untuk mendapatkan model terbaik digunakan uji lanjut, yaitu Uji Chow dan Uji Hausman. Dari hasil estimasi model diperoleh bahwa FEM sebagai model terbaik dalam penelitian ini. Lebih lanjut, hasil penelitian menemukan bahwa likuiditas aset berpengaruh positif dan tidak signifikan terhadap likuiditas saham, sedangkan financial leverage berpengaruh negatif dan signifikan terhadap likuiditas saham.</p>

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 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

scholarly journals PENENTUAN KARAKTERISTIK PARIWISATA DAN MODEL JUMLAH WISATAWAN UNTUK KABUPATEN/KOTA DI PROVINSI ACEH

2016 ◽  
Vol 16 (1) ◽  
pp. 43-50
Author(s):  
Nurhasanah Nurhasanah ◽  
Nany Salwa ◽  
Nelva Amelia
Keyword(s):  
Panel Data ◽  
Fixed Effect Model ◽  
Fixed Effect ◽  
Regional Government ◽  
Tourist Attractions ◽  
Tourist Destinations ◽  
Effect Model ◽  
Panel Data Regression ◽  
Data Regression ◽  
The Relationship

Tourism is one of the primary sectors that is expected to increase the regional government income. Therefore there is a need to observe the factors that affect the successfulness of tourism factors and products offered. Tourism products can be tourist destinations, where the characteristics of that particular destination can affect the decisions made by the tourist to return the place again. The characteristics of tourism in Aceh can be analyzed by using biplot analysis. Meanwhile, the effects of tourism characteristics on the number of tourists in Aceh from the year 2008 until 2013 is analyzed using panel data regression analysis that is approached by Fixed Effext Model (FEM). Based on the biplot graph, the cities that are superior in their number of all tourism products are Sabang and Banda Aceh. Cities other than these two cities tend to have a lower number of their tourism products. The biplot graph can explain the relationship between the variables of tourism products by 83.8%. Based on the model of fixed effect panel data, Aceh tourism products that affect the number of tourists in Aceh is the number of accommodations, restaurants, and tourist attractions. Fixed effect model explain correlation between the variables of tourism products to the number of tourists in Aceh by 78.8%.

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