scholarly journals THE IMPORTED INPUTS AND FIRM EXPORT PERFORMANCE IN INDONESIAN TEXTILE AND APPAREL INDUSTRIES

2020 ◽  
Vol 13 (2) ◽  
pp. 21-41
Author(s):  
Fransiskus Xaverius David Ardiyanto
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Export Performance ◽  
Effective Protection ◽  
Domestic Production ◽  
Protection Scheme ◽  
Textile And Apparel ◽  
Textile And Apparel Industries ◽  
The Government ◽  
Unbalanced Panel Data

Limiting imported inputs for Indonesian textile and apparel industries may inadvertently decelerate the industries’ export performance, because each subsector in the industries has its own characteristics. This study analyzes the use of imported inputs and firms’ exports in the Indonesian textile and apparel industries. It has employed unbalanced panel data from 2000–2015 with year gaps and estimated them using regression model. The main findings show that foreign input has a positive and significant impact on the firms’ exports, and the effect is larger on the apparels than the textiles when the industries are detangled. Although the result suggests a positive connection, the government may not fully liberalize all imported inputs for the industries. Instead, they may implement an effective protection scheme by relaxing tariffs on imported inputs for domestic production and imposing high tariffs imported inputs that have the potential to compete with domestic finished products.

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 Death Infectious: Impact of the Coronavirus Disease (COVID-19) on Stock Returns

2021 ◽  
Vol 24 (1) ◽  
pp. 95
Author(s):  
Robin Robin
Keyword(s):  
Panel Data ◽  
Mass Media ◽  
Stock Returns ◽  
Capital Market ◽  
Total Sample ◽  
Bad News ◽  
Market Returns ◽  
Independent Variables ◽  
The Government ◽  
Unbalanced Panel Data

This study examines the Coronavirus disease (COVID-19) on stock returns. The independent variables are daily new deaths and daily new cases. The sample that uses in this study is financial sector, one of the most crucial sectors in an economy. Total sample is 22,930 observations during the period from March to December in 2020. This study uses unbalanced panel data and multiple regression to prove those hypotheses. The result shows that the Coronavirus disease (COVID-19) hurt on stock returns. Investors feel anxious and frightened to hear the news regarding the increasing number of deaths and the number of new cases. Investors prefer to delay investment until the capital market returns to normal. Furthermore, during the pandemic period, Friday's effect may reduce losses from stock returns. The implication of this study is that an increase in the number of deaths and the number of new cases can reduce stock returns. The government needs to suppress bad news circulating in the mass media in order to reduce investor anxiety.  

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 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.  

PENGARUH PEMBANGUNAN INFRASTRUKTUR TERHADAP PDRB DI PROVINSI INDONESIA

Wahana ◽  
2019 ◽  
Vol 22 (1) ◽  
pp. 15-27
Author(s):  
Suripto Suripto ◽  
Eva Dwi Lestari
Keyword(s):  
Economic Growth ◽  
Economic Development ◽  
Panel Data ◽  
Infrastructure Investment ◽  
Water Infrastructure ◽  
Infrastructure Development ◽  
Road Infrastructure ◽  
Health Infrastructure ◽  
Spatial Coverage ◽  
The Government

Economic growth is one indicator to measure  the success of economic development in a country. Economic development is closely related to infrastructure. Infrastructure development will have an impact on economic growth both directly and indirectly. Therefore, the role of the government in determining infrastructure development policies is very important to increase economic growth in Indonesia. The purpose of this study is to determine the effect of infrastructure on economic growth in Indonesia including road infrastructure, electricity infrastructure, investment, water infrastructure, education infrastructure and health infrastructure in Indonesia in 2015-2017.The analytical tool used in this study is panel data regression with the approach of Fixed Effect Model. The spatial coverage of this study is all provinces in Indonesia, namely 34 provinces, with a series of data from 2015 to 2017 with a total of 102 observations. The data used is secondary data obtained from BPS Indonesia.The results of the study show that (1) the road infrastructure variables have a negative and not significant effect on GDRP. (2) electrical infrastructure variables have a negative and not significant effect on GDRP. (3) investment variables have a positive and significant effect on GDRP. (4) water infrastructure variables have a positive and not significant effect on GDRP. (5) educational infrastructure variables have a positive and not significant effect on GDRP. (6) health infrastructure variables have a positive and significant effect on GDRP. Keywords: development, infrastructure, investment, GDRP, panel data

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