scholarly journals Research on the Correlation Between Energy Reform and City Development Based on Cointegration and Causality Analysis Part 2 Interpretative Structure Model and Panel Data Regression Model

2020 ◽  
Vol 218 ◽  
pp. 02020
Author(s):  
Yan Zhang ◽  
Zhenwei Li ◽  
Zhe Xie ◽  
Luxin Zhao ◽  
Chen Zhang ◽  
...  
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Structure Model ◽  
Causality Analysis ◽  
City Development ◽  
Factors Affecting ◽  
Panel Data Regression ◽  
Data Regression ◽  
Industry Environment ◽  
Energy Reform

This article builds a model of energy-city development correlation, and factors for energy systems to drive city development. It also analyzes the corresponding indicators and determines the intercorrelation between the indicators. In the end, the article collects and categorizes energy, industry, environment, and other representative indicators from prefecture-level cities in China, and analyzes the factors affecting energy and city development based on the panel data regression model.

Determinants Of Capital Structure For Multinational And Domestic Companies In India: Panel Data Regression Model

2019 ◽  
Vol 118 (7) ◽  
pp. 147-154
Author(s):  
K. Maheswari ◽  
Dr. J. Gayathri ◽  
Dr. M. Babu ◽  
Dr.G. Indhumathi
Keyword(s):  
Panel Data ◽  
Capital Structure ◽  
Regression Model ◽  
Business Risk ◽  
Return On Assets ◽  
Panel Data Regression ◽  
Data Regression ◽  
Return On Capital ◽  
Domestic Companies ◽  
The Capital Structure

The capital structure refers to the components of capital needed to establish and expand its business activities. The study was made with an objective to examine the determinants of capital structure of multinational and domestic companies listed in S&P BSE automobile sector. The study concluded that there is significant impact on capital structure determinants such as size, business risk, non debt shield tax, return on assets, tangibility, profit, return on capital employed and liquidity on the capital structure of multinational and domestic companies of Indian Automobile Sector.  

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 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 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.

scholarly journals DETERMINAN INDEKS KEBAHAGIAAN DI INDONESIA

2021 ◽  
Vol 7 (2) ◽  
pp. 34
Author(s):  
Imawan Azhar Ben Atasoge
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Gini Index ◽  
Well Being ◽  
Panel Data Regression ◽  
Data Regression ◽  
Per Capita ◽  
Subjective States

ABSTRAK  Tolok ukur untuk melihat kemakmuran sebuah Negara dapat dilihat dari GDP yang ada di negara tersebut. Ukuran kesejahteraan tidak hanya diukur berdasarkan substansi akan tetapi diukur berdasarkan keadaan subjektif atau kebahagiaan. Tujuan dari penelitian ini ialah mengetahui faktor yang mempengaruhi kebahagiaan di Indonesia periode 2014 dan 2017. Analisis yang digunakan yaitu model regresi data panel. Penelitian ini menunjukkan bahwa variabel pendidikan, kesehatan, indeks gini serta zis berpengaruh secara terhadap kebahagiaan di Indonesia. Sedangkan variabel PDRB per kapita, kemiskinan, dan Indeks Demokrasi. Kata Kunci: Indeks Kebahagiaan, IPM, Kemiskinan, Indeks Gini, Zakat, Indeks Demokrasi ABSTRACTThe benchmark for seeing the prosperity of a country can be seen from the GDP in that country. The measure of well-being is not only measured based on substance but is measured based on subjective states or happiness. The purpose of this study is to determine the factors that influence happiness in Indonesia for the period 2014 and 2017. The analysis used is a panel data regression model. This study shows that the variables of education, health, Gini index and zis have a significant effect on happiness in Indonesia. While the variables are per capita GRDP, poverty, and the Democracy Index.Keywords: Happiness Index, HDI, Poverty, Gini Index, Zakat, Democracy Index

Non-performing Loans and Moral Hazard in the Indian Banking Sector: A Threshold Panel Regression Approach

2020 ◽  
pp. 097215092092613
Author(s):  
Robin Thomas ◽  
Shailesh Singh Thakur
Keyword(s):  
Panel Data ◽  
Moral Hazard ◽  
Regression Model ◽  
Fixed Effects ◽  
Bank Lending ◽  
Empirical Literature ◽  
Data Set ◽  
Panel Data Regression ◽  
Data Regression ◽  
Indian Banks

This article attempts to examine the effect of non-performing assets (NPA) on behaviour of banks in India. The objectives of this article is to test if lending choices of Indian Banks demonstrate moral hazard and to test whether an increase in NPA ratio of banks raises riskier bank lending. We employ a threshold panel data regression model on a data set retrieved from the Reserve bank of India, which covered 45 commercial banks during the period 2009–2015, to test if lending choices of Indian banks demonstrate moral hazard. The results establish that the moral hazard hypothesis does not hold true for the given sample of India Banks, suggesting that an increase in the NPA ratio does not potentially increase riskier lending in sample banks. We find empirical evidence for the notion that ‘too-big-to-fail’ banks possibly have certain incentives to take higher risks and thus have higher NPA ratios. Graphical approach to NPA threshold explanation reveals presence of threshold; however, it could not be statistically established. Future implications of findings are evaluated. The study seminally adds to the empirical literature on use of fixed effects threshold panel data regression model in the context of Indian banks.

scholarly journals Trends in COVID-19 Infected Cases Based on Panel Data Regression Modelling

2021 ◽  
Author(s):  
A. RAJARATHINAM ◽  
P TAMILSELVAN
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Random Effects ◽  
Tamil Nadu ◽  
Random Effects Model ◽  
Infected Case ◽  
Panel Data Regression ◽  
Data Regression ◽  
Tamil Nadu State ◽  
The Government

Abstract Background and Objective: The novel coronavirus pandemic, known as COVID-19, could not have been more predictable; thus, the world encountered health crises and substantial economic crises. This paper analysed the trends in COVID-19 cases in October 2020 in four southern districts of Tamil Nadu state, India, using a panel regression model. Materials and Methods: Panel data on the number of COVID-19-infected cases were collected from daily bulletins published through the official website www.stopcorona.tn.gov.in maintained by the Government of Tamil Nadu state, India. Panel data regression models were employed to study the trends. EViews Ver.11. Software was used to estimate the model and its parameters. Results: In all four districts, the COVID-19-infected case data followed a normal distribution. Maximum numbers of COVID-19-infected cases were registered in Kanniyakumari, followed by Tirunelveli, Thoothukudi and Tenkasi districts. The fewest COVID-19 cases were registered in Tenkasi, followed by Tirunelveli, Thoothukudi and Kanniyakumari districts. A random effects model was found to be an appropriate model to study the trend.Conclusion: The panel data regression model is found to be more appropriate than traditional models. The Hausman test and Wald test confirmed the selection of the random effects model. The Jarque-Bera normality test ensured the normality of the residuals. In all four districts under study, the number of COVID-19 infections showed a decreasing trend at a rate of 1.68% during October 2020.

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