scholarly journals DETERMINAN INFLASI DAN PENGANGGURAN DI NEGARA ASEAN

2020 ◽  
Vol 3 (2) ◽  
pp. 328-334
Author(s):  
Yufita Listiana ◽  
Sariyani
Keyword(s):  
World Bank ◽  
Cross Sections ◽  
Time Series Data ◽  
Series Data ◽  
The World Bank ◽  
Panel Data Regression ◽  
Data Regression ◽  
Unemployment Data ◽  
Asean Countries ◽  
The Government

Unemployment and inflation are macro problems that are often faced by a country. This problem can trigger social and political problems. This study aims to analyze the influence between inflation and unemployment in ASEAN countries. The data used are inflation and unemployment data taken from the World Bank website. This research method uses panel data regression, which is a regression model that combines 1991-1209 Time Series data and Cross Sections from 10 ASEAN countries. The results showed that unemployment has a significant effect on inflation in ASEAN countries, the government is expected to maintain inflation and unemployment stability.

scholarly journals FAKTOR – FAKTOR YANG MEMPENGARUHI KONSUMSI GAS DI INDONESIA

2021 ◽  
Vol 3 (2) ◽  
pp. 69
Author(s):  
Rohim Rohim ◽  
Mike Triani
Keyword(s):  
Time Series ◽  
World Bank ◽  
Time Series Data ◽  
Secondary Data ◽  
Series Data ◽  
Industrial Growth ◽  
A Value ◽  
The World Bank ◽  
The World ◽  
Gas Consumption

The purpose of this research is to determine (1) the effect of income on gas consumption in Indonesia (2) the effect of population on gas consumption in Indonesia (3) the effect of industrial growth on gas consumption in Indonesia. This type of research is descriptive and associative. The data used in this research is secondary data from Indonesia in the form of time series data from 1970 to 2019 and this data was obtained from official institutions of the World Bank and BP Statistic World. The data were processed using multiple linear regression. The results showed that the income had a negative and significant effect on gas consumption with a probability value of 0.0005 <0.05, the population had a positive and significant effect on gas consumption with a value of prob t-count of 0.0010 <0.05 and industrial growth had a positive and significant effect on gas consumption.  The significant to gas consumption in Indonesia with a value of prob t-count value of 0.5219 <0.05 and suggestions for further researchers to be able to analyze other factors that affecting gas consumption in Indonesia.  Because from the gas sectors, there are still many factors that affected gas consumption until the research results will be better

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.

Developing Public Sector Growth through E-Government and Democracy A Detailed Analysis

2020 ◽  
pp. 8-15
Author(s):  
Taufiq Gutawa
Keyword(s):  
Public Sector ◽  
Time Series Data ◽  
Series Data ◽  
Control Variables ◽  
Review Section ◽  
The Future ◽  
Efficiency And Effectiveness ◽  
Asean Countries ◽  
The Government ◽  
The Right

Public sector growth refers to the growth and development in the government-controlled departments and establishments. The industries and different sectors of a country that come under the influence of government come under public sector. E-government is actually the employment of innovative techniques and practices while performing several operations for the facilitation of citizens by the government. The core motive of using e-government practices to ensure the efficiency and effectiveness of those operations that are being performed for the public. Democracy refers to the right of citizens of a particular country in order to choose the leaders or government of their own choice based on the decision of majority. This study investigates promoting public sector growth through E-government adoption and democracy in ASEAN countries. Transparency rate and population factor are two important control variables which are induced in this research study. In the literature review section, previous related research studies have been indicated. The time-series data has been collected about concerned variables regarding ASEAN countries. The analyses portion includes unit root IPS, Pedroni cointegration and FMOLS regression and concluded that the hypotheses proposed by the researcher are accepted along with some share of impact of control variables. The researcher concluded that E-government and democracy positively impact public sector growth of ASEAN countries. At the last of this study, implications, limitations and future recommendations are also present. The implications include various theoretical, practical and policy making contexts. The future recommendations can be used by the future researchers so that they can increase the scope of their researches.

Financial Inclusion and Bank Efficiency in Indonesia

2020 ◽  
Vol 10 (2) ◽  
pp. 595
Author(s):  
Fida MUTHIA ◽  
Agung Putra RANEO ◽  
Sri ANDAIYANI
Keyword(s):  
Small And Medium Enterprises ◽  
Financial Inclusion ◽  
Data Envelopment ◽  
Bank Efficiency ◽  
The World Bank ◽  
Panel Data Regression ◽  
Data Regression ◽  
The Government ◽  
Using Data ◽  
Medium Enterprises

The aim of this study is to find out the effect of financial inclusion on bank efficiency in Indonesia. Data from 26 banks for the period of 2011 to 2016 is used to measure bank efficiency using Data Envelopment Analysis (DEA). While the data from the World Bank is used to calculate the ratio of outstanding loans of small and medium enterprises to total outstanding loans in banks to measure financial inclusion index. Panel data regression is done to analyze the effect and the result shows that financial inclusion has a positive and significant effect on bank efficiency where an increase in financial inclusion could improve bank efficiency. The result implies that the government must present a conducive financial environment for the implementation of the SNKI program that can improve financial inclusion and bank efficiency.  

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.

(Testing the causality between Exports, Oil Rent, and Gross Domestic Product in Kuwait: An empirical study for the period (1980 -2017: اختبار السببية بين الصادرات وإيجار النفط والناتج المحلي الإجمالي في الكويت: دراسة تجريبية للفترة (1980 - 2017)

2020 ◽  
Vol 4 (15) ◽  
Author(s):  
Saif Sallam Alhakimi
Keyword(s):  
Economic Growth ◽  
World Bank ◽  
Time Series Data ◽  
Series Data ◽  
The World Bank ◽  
Long Run ◽  
Causality Tests ◽  
Short Run ◽  
Direction Of Causation ◽  
The Relationship

The study of the relationship between oil prices, exports, and economic growth has captured the interest of economists for decades, especially for oil-exporting countries. This study intends to determine the relationship and the direction of causation among oil rent, exports, and economic growth in the short-run and long-run, and the causation effects among the variables. Time series data collected from both the world bank and International Monetary Fund databases for the period 1980 to 2017. The series tested for stationarity, cointegration, and causation using the unit root, cointegration, and pairwise granger causality tests. The results revealed that there was a long-run association among the variables. On the other hand, causation only exists between export and economic growth in both directions. Eviews10 statistical software used for the analysis.

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 TINJAUAN DEFISIT FISKAL, EKSPOR, IMPOR DAN JUMLAH UMKM TERHADAP PERTUMBUHAN EKONOMI INDONESIA

2020 ◽  
Vol 5 (2) ◽  
pp. 151
Author(s):  
Erni Febrina Harahap ◽  
Luviana Luviana ◽  
Nurul Huda
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Time Series Data ◽  
Estimation Method ◽  
Secondary Data ◽  
Fixed Effect ◽  
Series Data ◽  
Important Indicator ◽  
Panel Data Regression ◽  
Data Regression

<div class="WordSection1"><p><em>Economic growth is one of most important indicator in analyze the economic development in a state. This research purpose to analyze how much the influence of fiscal deficits, export, import, and total UMKM to Indonesian economic growth. The type of data used in this research is secondary data in the form of time series data and was obtained from some government institutions. The estimation method used is panel data regression with the fixed effect approach period 2010 - 2017. From the results of this research refer that fiscal deficit, import and total UMKM have a significant to Indonesian economic growth, while export not significant to Indonesian economic growth.</em></p></div><p>Pertumbuhan ekonomi merupakan salah satu indikator yang sangat penting dalam melakukan analisis pembanguan ekonomi di suatu negara. Penelitian ini bertujuan untuk menganalisis seberapa besar pengaruh defisit fiskal, ekspor, impor, jumlah UMKM terhadap Pertumbuhan Ekonomi Indonesia. Jenis data yang digunakan dalam penelitian ini adalah data sekunder yang berupa data <em>time series</em> dan diperoleh dari beberapa lembaga pemerintah. Metode estimasi yang digunakan adalah regresi data panel dengan pendekatan <em>fixed effect </em>periode 2010 – 2017. Dari hasil penelitian menunjukkan bahwa defisit fiskal, impor dan jumlah UMKM berpengaruh signifikan terhadap Pertumbuhan Ekonomi Indonesia, sedangkan ekspor tidak berpengaruh signifikan terhadap pertumbuhan ekonomi Indonesia.</p><p><strong> </strong></p>

scholarly journals Asean dan Pembangunan Ekonomi Maritim Kepulauan Riau – Indonesia

2021 ◽  
Vol 1 (2) ◽  
pp. 16-27
Author(s):  
Akhirman Akhirman
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Economic Development ◽  
World Bank ◽  
Time Series Data ◽  
The Philippines ◽  
Series Data ◽  
Brunei Darussalam ◽  
National Economic ◽  
Asean Countries

This study aims to find out the Maritime Economic Development of ASEAN Countries and Riau Islands - Indonesia. ASEAN which was established on August 8, 1967 through the Bangkok Declaration by Indonesia, the Philippines, Malaysia, Singapore, and Thailand. Then in 1984 he joined the State of Brunei Darussalam, in 1995 followed by the country of Vietnam, in 1997 the countries of Laos and Myanmar, and in 1998 joined the country of Cambodia. ASEAN in the Indonesian language known as Perbara or Perhimpunan Nations of Southeast Asia is a collaborative organization in the field of economy and geo-politics. The variables used in this study are Economic Growth, Export Rate. Inflation, and IPM. The data used is time series data, namely from 2014-2016. The analytical method used in this study is descriptive and econometric analysis. World Bank data, in 2017, predicts that there are three countries, namely Cambodia, Laos and Myanmar, which are predicted to have the most expansive economic growth after India in 2017-2019, and it is estimated that economic growth can reach 7%. while Indonesia in Quarter II 2017 grew 5.1 percent (BPS, 2017), while in 2013 it grew 5.58 percent. Riau Islands, a small town that captures part of NKRI in 2015 6.02 percent (yoy) economic growth, in the second quarter of 2017 must be willing with the lowest number two national economic growth, which is 2.02 percent which was the highest in Sumatra exceed national figures of 4.79 percent. (yoy). Suggestions given in this study include the need to think about a policy strategy that has potential economic areas to support sustainable export growth so that it can improve economic growth better.

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