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

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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Analisis Regresi Panel pada Pemodelan Tingkat Kematian Bayi di Provinsi Sulawesi Selatan

Journal of Mathematics, Computations, and Statistics ◽

10.35580/jmathcos.v2i2.12574 ◽

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.

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

Jurnal Gaussian ◽

10.14710/j.gauss.v9i3.28925 ◽

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.

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

GANEC SWARA ◽

10.35327/gara.v13i1.63 ◽

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.

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

E-Jurnal Matematika ◽

10.24843/mtk.2014.v03.i01.p059 ◽

2014 ◽

Vol 3 (1) ◽

pp. 1 ◽

Cited By ~ 3

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.

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

Jurnal Natural ◽

10.24815/jn.v16i1.4805 ◽

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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Determinan Produktivitas Tenaga Kerja Industri Manufaktur Besar dan Sedang di Pulau Jawa

Jurnal Ekonomi dan Pembangunan Indonesia ◽

10.21002/jepi.v21i2.1298 ◽

2021 ◽

Vol 21 (2) ◽

pp. 185-203

Author(s):

Rika Dwi Puspita Sari ◽

Siskarossa Ika Oktora

Keyword(s):

Panel Data ◽

Labor Productivity ◽

National Level ◽

Fixed Effect Model ◽

Fixed Effect ◽

Manufacturing Industries ◽

Real Wages ◽

Effect Model ◽

Panel Data Regression ◽

Data Regression

Industrialization is one of the government’s focuses on development. Java is an area focused on the industry. However, the labor productivity of large and medium manufacturing industries in Java is lower than regions outside Java and national level of productivity. This study aims to analyze determinants of labor productivity in large and medium manufacturing industries in all provinces in Java from 2010 to 2015 using panel data regression. As the best model, fixed effect model showed that HDI, real wages, and vehicle PMTB has a positively significant effect on labor productivity. -------------------------------------- Industrialisasi merupakan salah satu fokus pemerintah dalam pembangunan. Pulau Jawa merupakan wilayah yang difokuskan untuk industri. Namun, produktivitas tenaga kerja Industri Besar dan Sedang (IBS) di Pulau Jawa lebih rendah dibandingkan daerah di luar Pulau Jawa dan tingkat produktivitas nasional. Penelitian ini bertujuan untuk menganalisis determinan produktivitas tenaga kerja IBS seluruh provinsi di Pulau Jawa periode 2010–2015 dengan menggunakan metode regresi data panel. Hasil analisis menunjukkan Fixed Effect Model merupakan model terbaik untuk penelitian ini, dengan Indeks Pembangunan Manusia (IPM), upah riil, dan Pembentukan Modal Tetap Bruto (PMTB) kendaraan berpengaruh secara signifikan positif terhadap produktivitas tenaga kerja.

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ANALISIS REGRESI UNTUK DATA PANEL PADA PEMODELAN TINGKAT KEMATIAN IBU DI PROVINSI SULAWESI SELATAN

VARIANSI: Journal of Statistics and Its application on Teaching and Research ◽

10.35580/variansiunm14628 ◽

2020 ◽

Vol 2 (1) ◽

pp. 20

Author(s):

Muhammad Imran Rahman ◽

Muhammad Nusrang ◽

S. Sudarmin

Keyword(s):

Parameter Estimation ◽

Panel Data ◽

Regression Model ◽

Fixed Effect Model ◽

Fixed Effect ◽

Least Square ◽

South Sulawesi ◽

Effect Model ◽

Panel Data Regression ◽

Data Regression

Abstrak. Penelitian ini membahas mengenai estimasi parameter model regresi data panel pada pemodelan tingkat kematian ibu di Provinsi Sulawesi Selatan dari tahun 2014 sampai dengan 2016. Data yang digunakan adalah data sekunder dari Dinas Kesehatan Provinsi Sulawesi Selatan yang berupa jumlah kematian ibu, perdarahan, hipertensi dalam kehamilan, infeksi dan gangguan sistem peredaran darah di seluruh Kabupaten/Kota di Provinsi Sulawesi Selatan tahun 2014-2016. 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 dengan nilai 𝑅2 = 90%. Adapun peubahpeubah yang berpengaruh signifikan terhadap kematian ibu adalah perdarahan, hipertensi dalam kehamilan dan infeksi. Dari hasil analisis diperoleh juga daerah yang memiliki jumlah kematian ibu terbesar di Provinsi Sulawesi Selatan tahun 2014-2016 adalah Bone dan Jeneponto.Kata Kunci: Regresi data Panel, Angka Kematian Ibu, Fixed Effect Model, Least Square Dummy Variable.Abstract. This research discusses about parameter estimation of panel data regression model of mother mortality level modelling in South Sulawesi from 2014 to 2016. The data used were secondary data from Dinas Kesehatan Provinsi Sulawesi Selatan in the form of number of mother mortality, bleeding, infection, circulatory system disorders and metabolic disorders in the whole district/town in South Sulawesi year 2014-2016. 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 with value of 𝑅2 = 90%. The variables that significantly influence maternal mortality are bleeding, hypertension in pregnancy and infection. From the results of the analysis, it was also found that the regions that had the largest number of maternal deaths in South Sulawesi Province in 2014-2016 were Bone and Jeneponto.Keywords: Panel Data Regression, Mother Mortality Rate, Fixed Effect Model, Least Square Dummy Variable.

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STRATEGI ALOKASI BELANJA PEMERINTAH DAERAH UNTUK MENINGKATKAN INDEKS PENDIDIKAN DI PROVINSI BANTEN

Jurnal Manajemen Pembangunan Daerah ◽

10.29244/jurnal_mpd.v10i-.22711 ◽

2018 ◽

Vol 10 ◽

Author(s):

Abdullah Al Mizan ◽

A. Faroby Falatehan ◽

Ekawati Sri Wahyuni

Keyword(s):

High Schools ◽

Panel Data ◽

Descriptive Analysis ◽

Budget Allocation ◽

Fixed Effect Model ◽

School Enrollment ◽

Fixed Effect ◽

Effect Model ◽

Panel Data Regression ◽

Data Regression

ABSTRACTHuman development measured by Human Development Index (HDI) is composed of three components: health index, education index, and expenditure index. HDI Banten on year 2016 was ranked 8th in Indonesia, but the education index has the lowest value among the other components. Whereas education is a capital which is very important for people to achieve better welfare. This study aimed to formulate strategies to improve education index through education budget allocation in Banten Province. The analytical methods used were descriptive analysis, panel data regression analysis, SWOT analysis, and QSPM. Descriptive analysis was used to give an overview of the education condition in Banten Province. By using fixed effect model panel data regression, found that income per capita, School Enrollment Rate of high schools level, Pupil-Teacher Ratio of high schools level, and Number of high schools had positive and significant influences on education index in Banten Province. Meanwhile, based on the interview results of key respondents, by using SWOT techniques obtained six grand strategies that could improve the education index. The strategies obtained were analyzed using QSPM. The QSPM results showed the priority strategies for increasing the education index was by the policy improvement and increase in the budget allocation in the education sector.Key words: HDI, education index, SWOT, QSPMABSTRAKPembangunan manusia yang diukur melalui Indeks Pembangunan Manusia (IPM) dibentuk dari tiga komponen yakni indeks kesehatan, indeks pendidikan, dan indeks pengeluaran. IPM Banten Tahun 2016 menduduki peringkat 8 terbesar di Indonesia Namun demikian, indeks pendidikan memiliki nilai yang paling rendah di antara komponen lainnya. Padahal pendidikan merupakan salah satu modal yang sangat penting bagi seseorang untuk menuju kesejahteraan yang lebih baik. Penelitian ini bertujuan untuk merumuskan strategi meningkatkan indeks pendidikan melalui alokasi anggaran pendidikan. di Provinsi Banten Metode analisis yang digunakan yakni analisis deskriptif, analisis regresi data panel, analisis SWOT, dan QSPM. Analisis deskriptif digunakan untuk memberikan gambaran umum pendidikan di Provinsi Banten. Sedangkan dengan menggunakan regresi data panel fixed effect model didapat hasil bahwa pendapatan perkapita, Angka Partisipasi Sekolah (APS) tingkat SMA, Rasio Murid per Guru (RMG) tingkat SMA, dan Jumlah SMA memiliki pengaruh yang positif dan signifikan terhadap indeks pendidikan di Provinsi Banten. Sementara itu berdasarkan hasil wawancara kepada responden kunci, melalui teknik SWOT diperoleh enam strategi yang dapat meningkatkan indeks pendidikan. Strategi yang didapat kemudian dianalisis menggunakan QSPM. Hasil analisis QSPM menunjukkan bahwa strategi prioritas untuk meningkatkan indeks pendidikan yakni melalui penyempurnaan kebijakan dan peningkatan alokasi anggaran di bidang pendidikan. Kata kunci: IPM, Indeks Pendidikan, SWOT, QSPM

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Analisis Pembiayaan Mudharabah Bank Pembiayaan Rakyat Syariah Terhadap Nonperforming Financing di Indonesia Tahun 2015-2020

Jurnal BAABU AL-ILMI: Ekonomi dan Perbankan Syariah ◽

10.29300/ba.v6i1.4106 ◽

2021 ◽

Vol 6 (1) ◽

pp. 26

Author(s):

Andiman Andiman ◽

Agus Widardjono

Keyword(s):

Panel Data ◽

Model Selection ◽

Research Method ◽

Fixed Effect Model ◽

Fixed Effect ◽

Effect Model ◽

Panel Data Regression ◽

Data Regression ◽

Negative Effect ◽

Selection Test

This study aims to analyze the effect of the type of financing on the Non-Performing Financing (NPF) of Islamic People's Financing Banks in Indonesia for the 2015-2019 period. The research method used is panel data regression with the Fixed Effect model as the recommended model based on the results of the model selection test. The results showed that the Log Mudharabah variable had a negative effect on the Non Performing Financing of Islamic Rural Banks. Mudharabah Log, Non Performing Financing

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Pengaruh ketimpangan pendapatan dan kemiskinan terhadap pertumbuhan ekonomi antar pulau di Indonesia

Jurnal Paradigma Ekonomika ◽

10.22437/paradigma.v14i2.6948 ◽

2019 ◽

Vol 14 (2) ◽

pp. 55-66

Author(s):

Selamet Rahmadi ◽

Parmadi Parmadi

Keyword(s):

Economic Growth ◽

Fixed Effect Model ◽

Model Estimation ◽

Estimation Model ◽

Hausman Test ◽

Pooled Data ◽

Effect Model ◽

Panel Data Regression ◽

Data Regression ◽

Estimation Models

This study aims to find out and analyze the effect of income inequality and poverty on economic growth on each island in Indonesia. To answer these objectives, panel data regression (pooled data) is used. The results of the study show: (1). the best regressions on estimation models are based on the Chow and the Hausman test using the Fixed Effect Model estimation model for each island in Indonesia. (2). Income and poverty inequality negatively affected economic growth in all islands in Indonesia during the 2015-2018.

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