scholarly journals Panel Data Modelling of COVID-19 Infected Cases

2021 ◽  
Author(s):  
RAJARATHINAM ARUNACHALAM ◽  
TAMILSELVAN PAKKIRISAMY
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Tamil Nadu ◽  
Wald Test ◽  
Infected Case ◽  
Panel Data Regression ◽  
Data Regression ◽  
Tamil Nadu State ◽  
The Government ◽  
Novel Coronavirus

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 analyses 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 e2ffects 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.

scholarly journals Panel Data Modelling of COVID-19 Infected Cases

2021 ◽  
Author(s):  
RAJARATHINAM ARUNACHALAM ◽  
TAMILSELVAN PAKKIRISAMY
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Tamil Nadu ◽  
Wald Test ◽  
Infected Case ◽  
Panel Data Regression ◽  
Data Regression ◽  
Tamil Nadu State ◽  
The Government ◽  
Novel Coronavirus

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 analyses 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 e2ffects 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.

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.

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

2021 ◽  
Author(s):  
RAJARATHINAM ARUNACHALAM ◽  
TAMILSELVAN PAKKIRISAMY
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.

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 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 Indonesian Clove Competitiveness and Competitor Countries in International Market

2020 ◽  
Vol 9 (1) ◽  
pp. 39-54
Author(s):  
Adnan Putra Pratama ◽  
Dwidjono Hadi Darwanto ◽  
Masyhuri Masyhuri
Keyword(s):  
Panel Data ◽  
Sri Lanka ◽  
Market Share ◽  
Secondary Data ◽  
International Market ◽  
Panel Data Regression ◽  
Data Regression ◽  
The World ◽  
The Government ◽  
Star Position

Trade liberalization is currently demanding every country to increase the competitiveness of its products. Indonesia as the largest clove producer in the world has a major competitor in the international market. This study aims to determine the competitiveness of Indonesia's clove exports and competing countries in the international market and determine the factors that affect its competitiveness. The data used in this study are secondary data from five major producing countries namely Indonesia, Madagascar, Tanzania, Sri Lanka, and Comoros during the period 2000-2017 sourced from UNComtrade, FAO and the World Bank. Competitiveness is measured by Revealed Comparative Advantage (RCA), Acceleration Ratio (AR) and Export Product Dynamic (EPD) while the factors that affect competitiveness are used panel data regression methods using E-Views software. The results showed that Indonesia had the lowest RCA index, the AR value showed Madagascar and Tanzania were able to capture market share in the international market and the EPD value showed that all countries occupied the rising star position except Sri Lanka in the falling star position. Panel data regression analysis results show that the market share and GDP variables significantly influence the competitiveness of the main clove producing countries while the production variables and export prices do not significantly influence the country's competitiveness. The government must dare to take policies to limit clove imports and increase exports.

scholarly journals PANEL DATA REGRESSION ANALYSIS FOR FACTORS AFFECTING INFANT MORTALITY RATE IN EAST JAVA 2013-2017

2021 ◽  
Vol 16 (3) ◽  
pp. 437
Author(s):  
Faishal Azhar Wardhana ◽  
Rachmah Indawati
Keyword(s):  
Panel Data ◽  
Mortality Rate ◽  
Infant Mortality ◽  
Data Availability ◽  
Infant Mortality Rate ◽  
Complete Coverage ◽  
Cross Sectional ◽  
Panel Data Regression ◽  
Data Regression ◽  
The Government

ABSTRACTThe escalating infant mortality rate (IMR) in Indonesia has not been able to fulfill the target of Sustainable Development Goals (SDGs) that restrict the limit of IMR to just 12 of 1,000 live births. According to such fact, this research was designed as the application of panel data regression in an IMR case study of East Java from 2013–2017. Regression panel data enable research in describing cross-sectional and time series information. The variety of data availability in this method were capable of producing a high degree of freedom, allowing it to meet the prerequisites and statistical properties. This method was considered the most suitable one for analyzing the rising IMR. This research was classified as non-reactive research. All regencies/cities in East Java served as this study’s population. Data collection included K4 coverage, childbirth assistance, and KN complete coverage. The result of panel data regression showed a significant connection between K4 coverage (0.0230), childbirth assistance (p = 0.0105), and KN complete coverage (0.0205). Adjusted R-Square value was obtained with an amount of 80%, which means that all independent variables were able to explain the dependent one of that value, while the remaining were explained by other factors. This study can provide some suggestions to support IMR in East Java, including handling from the government or related pregnant families to support IMR on an ongoing basis. Keywords: panel data regression, IMR, K4, childbirth assistance, KN complete

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

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