scholarly journals ANALISIS FAKTOR-FAKTOR YANG MEMPENGARUHI PENYERAPAN TENAGA KERJA DI PROVINSI JAWA TENGAH

2021 ◽  
Vol 13 (2) ◽  
pp. 141
Author(s):  
Shafa Nanda Puspita ◽  
Sri Maryani ◽  
Herry Purwantho
Keyword(s):  
Time Series Data ◽  
National Income ◽  
Series Data ◽  
Section Data ◽  
Central Java ◽  
Panel Data Regression ◽  
Data Regression ◽  
Lm Test ◽  
Selection Test ◽  
Central Java Province

Labor absorption is an important factor in supporting economic development through national income. The low level of employment is still a problem in various regions in Indonesia, especially in Central Java Province. The problem of employment, can be overcome by maximizing the factors that affect the increase in labor absorption. Therefore, it is necessary to analyze the factors that are thought to affect the increase in labor absorption. This study aims to analyze the factors that influence labor absorption in Central Java Province. This study uses a descriptive quantitative approach with a panel data regression model. The best model selection test used is the Chow test, Hausman test, and Lagrange Multiplier test (LM) Test which was carried out using the Eviews 9 software. This study uses cross section data from 35 districts/cities in Central Java Province and time series data on the number of workers, labor force, unemployment, minimum wages, and GRDP of each district/city for the 2015-2020 period. The results of the discussion show that simultaneously and partially the number of workers, the number of the workforce, the number of unemployed, the minimum wage, and GRDP have an effect on the absorption of labor in Central Java Province.

scholarly journals ANALYSIS OF WELFARE IN INDONESIA: CASE STUDY IN 2008-2017

2019 ◽  
Author(s):  
Basri Bado
Keyword(s):  
Natural Resources ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Income Per Capita ◽  
Section Data ◽  
Panel Data Regression ◽  
Data Regression ◽  
Per Capita

<p>The purpose of the study was to analyze the factors of natural resources, income per capita, infrastructure, education, institutions and population against inequality between regions and welfare in Indonesia. This study uses panel data regression analysis. This study analyzes secondary data consisting of 33 provincial cross section data and 10 years time series data (2008-2017).<br>The results of the study found inequality between regions in Indonesia with different intensities. Factors of natural resources, income per capita, infrastructure, education, wealth and population have a positive and significant effect on inequality between Factors of natural resources, income per capita, infrastructure, education, wealth and population have a positive and significant effect on inequality between regions. Furthermore, 2% of the inequality variables between regions affect the level of welfare and the rest are influenced by natural resources, per capita income, infrastructure, education, institutions and population.</p>

scholarly journals PENGARUH KINERJA KEUANGAN DAN KINERJA LINGKUNGAN TERHADAP NILAI PERUSAHAAN PERTAMBANGAN DI ISSI PERIODE 2013-2017

2020 ◽  
Vol 6 (9) ◽  
pp. 1795
Author(s):  
Lidia Ralina ◽  
Ari Prasetyo
Keyword(s):  
Firm Value ◽  
Time Series Data ◽  
Random Effect ◽  
Social Reporting ◽  
Series Data ◽  
Estimation Model ◽  
Current Ratio ◽  
Section Data ◽  
Panel Data Regression ◽  
Data Regression

This study analyzed the influence of Islamic Social Reporting (ISR), Return on Asset, and Current Ratio on the value of the companies which were registered in ISSI in 2013-2017. This research used panel data regression analysis to combine time series data and cross section data. The results of the research with estimation model of Random Effect showed that ISR, ROA and CR partially had significant effects on the value of the company. ISR, ROA and CR silmutanously affect on the value of the mining companies registered in ISSI 2013-2017.Keywords: Islamic Social Reporting, Return on Asset, Current Ratio, and Firm Value

scholarly journals ANALYSIS OF WELFARE IN INDONESIA: CASE STUDY IN 2008-2017

2019 ◽  
Author(s):  
Basri Bado
Keyword(s):  
Natural Resources ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Income Per Capita ◽  
Section Data ◽  
Panel Data Regression ◽  
Data Regression ◽  
Per Capita

<p>The purpose of the study was to analyze the factors of natural resources, income per capita, infrastructure, education, institutions and population against inequality between regions and welfare in Indonesia. This study uses panel data regression analysis. This study analyzes secondary data consisting of 33 provincial cross section data and 10 years time series data (2008-2017).<br>The results of the study found inequality between regions in Indonesia with different intensities. Factors of natural resources, income per capita, infrastructure, education, wealth and population have a positive and significant effect on inequality between Factors of natural resources, income per capita, infrastructure, education, wealth and population have a positive and significant effect on inequality between regions. Furthermore, 2% of the inequality variables between regions affect the level of welfare and the rest are influenced by natural resources, per capita income, infrastructure, education, institutions and population.</p>

scholarly journals Pemodelan Kasus DBD di Provinsi Jawa Timur dengan Metode Data Panel

2019 ◽  
Vol 8 (2) ◽  
pp. 101
Author(s):  
Annisa Dwinda Shafira
Keyword(s):  
Panel Data ◽  
Population Density ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Panel Regression ◽  
Affecting Factors ◽  
Factors Affecting ◽  
Panel Data Regression ◽  
Data Regression

The combination of panel data regression consist of time series data, it was collected based on a characteristic at a certain time (cross section). This research aimed to analyze the affecting factors and dominant factors of Dengue Hemoragic Fever (DHF) cases in East Java using panel data regression. This research uses secondary data published by the East Java Provincial Health Office, namely the Health Profile and the East Java Provincial Statistics Agency such as documents of each Districts/City in Numbers of East Java on 2014––2017 using total research population that were collected in all districts/cities in East Java Province. The data of new cases of DHF and factors affecting the incidence of DHF including clean and healthy living behavior in the household, poverty, population density, rainfall in East Java on 2014––2017. Panel regression analysis is used to determine the best model of the CEM, FEM and REM using Chow test, Hausman test and Langrange Multiplier test. Based on the results, the best model of panel regression is FEM with affecting variables such as poverty, population density, and rainfall.

scholarly journals The Effect of Inequality of Educational Facilities, Health, and Road Conditions toward Income Distribution in Central Java Province, Indonesia

2018 ◽  
Vol 73 ◽  
pp. 10014
Author(s):  
Antono Herry ◽  
Purnomo Adhi ◽  
Firmansyah
Keyword(s):  
Income Distribution ◽  
Gini Index ◽  
Time Series Data ◽  
Series Data ◽  
Educational Facilities ◽  
Public Facilities ◽  
Central Java ◽  
Road Condition ◽  
The Relationship ◽  
Central Java Province

This study examines the effect of inequality of public facilities, namely education, health, and road condition, on the income inequality in Central Java Province, Indonesia. By employing the time-series data of 15 years, this study analyzes the Gini index and the relationship between the Gini index and Index of public facilities by the regression model. The study finds that the inequality of the provision of public facilities affects the income distribution in Central Java, Indonesia

scholarly journals The Effect of General Allocation Funds and Local Own-Revenue on Regional Government Expenditure in Central Java Province

2021 ◽  
Vol 2 (1) ◽  
pp. 257-266
Author(s):  
Samsul Afif Rahula ◽  
Prasetyo Ari Bowo
Keyword(s):  
Central Government ◽  
Time Series Data ◽  
Government Expenditure ◽  
Series Data ◽  
Regional Government ◽  
Significant Positive Effect ◽  
Central Java ◽  
High Dependency ◽  
Positive Effect ◽  
Central Java Province

The implementation of regional autonomy indirectly requires regions to be independent, the level of independency in Central Java which is measured by the ratio of Local Own-source Revenue to total regional revenue of 18.2%, which is the lowest compared to other regions in Java in the 2015-2018 period. Central Java's low independency is in line with its dependency on transfers from the central government, particularly the general allocation fund of 40%. Low independency and high dependency is due to the lack of ability of Local Own-source Revenue to meet regional expenditures and the large role of general allocation fund in meeting these shortages. This study aims to analyze the effect of general allocation funds and local own-source revenue on regional expenditures. This study uses a quantitative method with panel data combination of cross-section data from 35 districts/cities in Central Java Province and time-series data for the period 2016-2018. The analytical method used is multiple linear regression. The results of this study indicate that the general allocation fund has a significant and more dominant positive effect on regional expenditures and also local own-source revenue shows a significant positive effect on regional expenditures, which means that the greater the value of local own-source revenue and general allocation fund, the greater the value of regional expenditures. Simultaneously, general allocation funds and local own-source revenue have a significant positive effect on regional expenditures.

scholarly journals Kluster Tanaman Pangan Unggulan di Provinsi Jawa Tengah

2021 ◽  
Vol 21 (1) ◽  
pp. 55-68
Author(s):  
Choiroel Woestho ◽  
Milda Handayani ◽  
Adi Wibowo Noor Fikri
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Food Plants ◽  
Series Data ◽  
Food Crops ◽  
Food Crop ◽  
Land Area ◽  
Location Quotient ◽  
Central Java ◽  
Central Java Province

The food crop sector has an important role for regions in Indonesia. Food plants can be a determinant for an area in meeting the needs of the people in that area. In addition, the food crop sector, if developed, can become revenue for the region. This study aims to analyze the leading food plants in 35 districts / cities in Central Java Province. By using the location quotient (LQ) method and the Regional Specialization Index. The data used is time series data from 2014 to 2019 in 35 districts / cities in Central Java Province for food crops based on land area and production. The results obtained for the average LQ value of food crops based on land area, there are only 12 districts / cities which are the basis for superior food crops with Wonogiri Regency at the top. Meanwhile, based on the average LQ value based on production, only 11 districts / cities are the basis for superior food crops with Semarang Regency being the top. For the specialization index based on both land area and production, there is no Regency / City that specializes in Central Java Province.   Keywords: Foodcrop Sector, Location Quotient, Specialization Index, Central Java   Abstrak   Sektor tanaman pangan mempunyai peranan penting bagi daerah di Indonesia. Tanaman pangan dapat menjadi penentu bagi suatu daerah dalam memenuhi kebutuhan masyarakat yang ada di daerah tersebut. Selain itu, sektor tanaman pangan jika dikembangkan dapat menjadi pendapatan bagi daerah. Penelitian ini bertujuan untuk menganalisis tanaman pangan unggulan yang ada di 35 Kabupaten/Kota pada Provinsi Jawa Tengah. Dengan menggunakan metode location quotient (LQ) dan Indeks Spesialisasi Regional. Data yang digunakan adalah data time series selama tahun 2014 hingga tahun 2019 pada 35 Kabupaten/Kota di Provinsi Jawa Tengah untuk tanaman pangan berdasarkan luas lahan dan produksi. Hasil yang diperoleh untuk nilai rata – rata LQ tanaman pangan berdasarkan luas lahan, hanya terdapat 12 Kabupaten/Kota yang menjadi basis bagi tanaman pangan unggulan dengan Kabupaten Wonogiri berada di urutan teratas. Sementara berdasarkan nilai rata – rata LQ berdasarkan produksi, hanya 11 Kabupaten/Kota yang menjadi basis tanaman pangan unggulan dengan Kabupaten Semarang menjadi urutan teratas. Untuk indeks spesialisasi baik berdasarkan luas lahan dan produksi, tidak ada Kabupaten/Kota yang mempunyai spesialisasi terhadap Provinsi Jawa Tengah.   Kata kunci: Tanaman Pangan, Indeks Lokalisasi, Indeks Spesialisasi, Jawa Tengah

scholarly journals Estimation Model And Selection Method Of Panel Data Regression : An Overview Of Common Effect, Fixed Effect, And Random Effect Model

2018 ◽  
Author(s):  
rizka zulfikar
Keyword(s):  
Time Series ◽  
Panel Data ◽  
Cross Section ◽  
Fuzzy Time Series ◽  
Series Data ◽  
Unbiased Estimation ◽  
Cross Section Data ◽  
Section Data ◽  
Panel Data Regression ◽  
Data Regression

The estimation in the regression analysis with cross section data is done by estimating the least squares method called Ordinary Least Square (OLS). Regression Method Data Panel will give the result of estimation which is Best Linear Unbiased Estimation (BLUE). Data Panel Regression is a combination of cross section data and time series, where the same unit cross section is measured at different times. So in other words, panel data is data from some of the same individuals observed in a certain period of time. If we have T time periods (t = 1,2, ..., T) and N the number of individuals (i = 1,2, ..., N), then with panel data we will have total observation units of N x T. If sum unit time is the same for each individual, then the data is called balanced panel. If instead, the number of time units is different for each individual, then it is called the unbalanced panel. While other data types, namely:time-series data and cross-section. In time series, one or more variables will be observed on one observation unit within a certain time frame. While data cross-section is the observation of several units of observation in a single point of time.Unlike the usual regression, panel data regression must go through the precise estimation modeling step.SEE ALSO :Zulfikar, R., &amp; Mayvita, P. A. (2017). THE EFFECTS OF POLITICAL EVENTS AGAINST ABNORMAL RETURN AND TOTAL VOLUME SHARIA SHARES ACTIVITY THAT LISTED IN JAKARTA ISLAMIC INDEX (JII). JEMA: Jurnal Ilmiah Bidang Akuntansi dan Manajemen, 14(02), 64-74.Zulfikar, R., &amp; AdeMayvita, P. (2017). Pengujian Metode Fuzzy Time Series Chen dan Hsu Untuk Meramalkan Nilai Indeks Bursa Saham Syariah Di Jakarta Islamic Index (JII). Wiga: Jurnal Penelitian Ilmu Ekonomi, 7(2), 108-124.

scholarly journals Pemodelan Kasus DBD di Provinsi Jawa Timur dengan Metode Data Panel

2019 ◽  
Vol 8 (2) ◽  
pp. 101
Author(s):  
Annisa Dwinda Shafira
Keyword(s):  
Panel Data ◽  
Population Density ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Panel Regression ◽  
Affecting Factors ◽  
Factors Affecting ◽  
Panel Data Regression ◽  
Data Regression

The combination of panel data regression consist of time series data, it was collected based on a characteristic at a certain time (cross section). This research aimed to analyze the affecting factors and dominant factors of Dengue Hemoragic Fever (DHF) cases in East Java using panel data regression. This research uses secondary data published by the East Java Provincial Health Office, namely the Health Profile and the East Java Provincial Statistics Agency such as documents of each Districts/City in Numbers of East Java on 2014––2017 using total research population that were collected in all districts/cities in East Java Province. The data of new cases of DHF and factors affecting the incidence of DHF including clean and healthy living behavior in the household, poverty, population density, rainfall in East Java on 2014––2017. Panel regression analysis is used to determine the best model of the CEM, FEM and REM using Chow test, Hausman test and Langrange Multiplier test. Based on the results, the best model of panel regression is FEM with affecting variables such as poverty, population density, and rainfall.

scholarly journals PENERAPAN ANALISIS REGRESI DATA PANEL PADA INDEKS PEMBANGUNAN MANUSIA DI PROVINSI PAPUA BARAT

2020 ◽  
Vol 16 (2) ◽  
pp. 120-128
Author(s):  
Desti Setya Ningsih ◽  
Esther Ria Matulessy ◽  
Dariani Matualage
Keyword(s):  
Regression Analysis ◽  
Panel Data ◽  
Time Series Data ◽  
Equation Model ◽  
Series Data ◽  
Model Parameters ◽  
Test Model ◽  
Panel Data Regression ◽  
Data Regression ◽  
West Papua

Panel Data Regression Analysis is a combination of time series data and cross section data. The purpose of this study is to determine the best model for panel data regression analysis on HDI in West Papua Province and to determine the HDI model in West Papua Province. The data used in this study are West Papua data in the 2019 Publication Figures and 2019 Publication Human Development Index data. In the process of determining the best model, estimating model parameters with 3 approaches namely CEM, FEM and REM, then testing model selection, classical assumption test, model equation checking and finally model interpretation. The results of this study indicate that the best regression model is FEM with individual effects and time effects with a good model of 91% which means that HDI in West Papua Province is explained by GRDP, RLS, JPM and UHH. The equation model is as follows: Based on the equations that have been obtained, the variables that have a significant effect on HDI in West Papua Province are RLS and UHH.

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