scholarly journals On the Capital Structure of Foreign Subsidiaries: Evidence from a Panel Data Quantile Regression Model

2021 ◽  
Author(s):  
Raffaele Miniaci ◽  
Paolo Panteghini
Keyword(s):  
Panel Data ◽  
Capital Structure ◽  
Quantile Regression ◽  
Regression Model ◽  
Foreign Subsidiaries ◽  
Quantile Regression Model ◽  
The Capital Structure

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 Application of a Panel Data Quantile-Regression Model to the Dynamics of Carbon Sequestration in Pinus kesiya var. langbianensis Natural Forests

Forests ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 12
Author(s):  
Chang Liu ◽  
Guanglong Ou ◽  
Yao Fu ◽  
Chengcheng Zhang ◽  
Cairong Yue
Keyword(s):  
Carbon Sequestration ◽  
Panel Data ◽  
Quantile Regression ◽  
Regression Model ◽  
Carbon Storage ◽  
Forest Carbon ◽  
Natural Forests ◽  
Pinus Kesiya ◽  
Quantile Regression Model ◽  
Crown Density

Even though studies on forest carbon storage are relatively mature, dynamic changes in carbon sequestration have been insufficiently researched. Therefore, we used panel data from 81 Pinus kesiya var. langbianensis forest sample plots measured on three occasions to build an ordinary regression model and a quantile-regression model to estimate carbon sequestration over time. In the models, the average carbon reserve of the natural forests was taken as the dependent variable and the average diameter at breast height (DBH), crown density, and altitude as independent variables. The effects of the DBH and crown density on the average carbon storage differed considerably among different age groups and with time, while the effect of altitude had a relatively insignificant influence. Compared with the ordinary model, the quantile-regression model was more accurate in residual and predictive analyses and removed large errors generated by the ordinary model in fitting for young-aged and over-mature forests. We are the first to introduce panel-data-based modeling to forestry research, and it appears to provide a new solution to better grasp change laws for forest carbon sequestration.

scholarly journals Determinants of Capital Structure: A Quantile Regression Analysis

2015 ◽  
Vol 10 (1) ◽  
pp. 16-34 ◽  
Author(s):  
Kumar Tiwari Aviral ◽  
Krishnankutty Raveesh
Keyword(s):  
Regression Analysis ◽  
Panel Data ◽  
Capital Structure ◽  
Quantile Regression ◽  
Negative Sign ◽  
Positive Sign ◽  
Quantile Regression Analysis ◽  
The Capital Structure

Abstract In this study, we attempted to analyze the determinants of capital structure for Indian firms using a panel framework and to investigate whether the capital structure models derived from Western settings provide convincing explanations for capital structure decisions of the Indian firms. The investigation is performed using balanced panel data procedures for a sample 298 firms (from the BSE 500 firms based on the availability of data) during 2001-2010. We found that for lowest quantile LnSales and TANGIT are significant with positive sign and NDTS and PROFIT are significant with negative sign. However, in case of 0.25th quantile LnSales and LnTA are significant with positive sign and PROFIT is significant with negative sign. For median quantile PROFIT is found to be significant with negative sign and TANGIT is significant with positive sign. For 0.75th quantile, in model one, LnSales and PROFIT are significant with negative sign and TANGIT and GROWTHTA are significant with positive sign whereas, in model two, results of 0.75th quantile are similar to the median quantile of model two. For the highest quantile, in case of model one, results are similar to the case of 0.75th quantile with exception that now GROWTHTA in model one (and GROWTHSA in model two).

scholarly journals Capital Structure Determinants of S&P BSE 500: A Panel Data Research

2019 ◽  
Vol 8 (2S7) ◽  
pp. 377-380 ◽  
Keyword(s):  
Panel Data ◽  
Capital Structure ◽  
Regression Model ◽  
Firm Size ◽  
Explanatory Variables ◽  
Industrial Sectors ◽  
The Capital Structure

The paper identifies the most important factors specific to companies which impacts on the capital structure of 416 companies belonging to 14 industrial sectors listed in S&P BSE 500 for a duration of 19 years which is from 2000 to 2018. Multi regression model is used to understand the influence of select variables on capital structure. The study finds that 4 explanatory variables like firm size, tax paid, depreciation to total assets ratio and profitability ratio are statistically significant capital structure determinants

Gender diversity and financial performance in an emerging economy: empirical evidencefrom India

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Santi Gopal Maji ◽  
Rupjyoti Saha
Keyword(s):  
Firm Performance ◽  
Panel Data ◽  
Quantile Regression ◽  
Regression Model ◽  
Financial Performance ◽  
Positive Impact ◽  
Gender Diversity ◽  
Content Type ◽  
Quantile Regression Model ◽  
The Impact

Purpose This paper aims to examine the impact of gender diversity both at operational and leadership levels on the financial performance of firms in India. Design/methodology/approach The study is based on a panel data set of 100 large Indian corporate firms. This study uses the Blau index and Shannon index to compute gender diversity. First, this paper uses system generalized method of moments model to deal with the potential endogeneity issue in the association between gender diversity and firm performance. Second, to unveil heterogeneity in such a relationship, the study applies panel data quantile regression model. Finally, the study adopts a generalized estimating equation model to investigate such relationships for group affiliated and standalone firms. Findings This study finds a significant positive impact of workforce gender diversity and board gender diversity on the financial performance of firms. Further, the results of the quantile regression model indicate that the impact of gender diversity (workforce and board) on firm performance is more pronounced at higher quantiles of the conditional distribution of firm performance. However, the study fails to extricate any significant impact of audit committee gender diversity on firm performance. Finally, the study also finds a significant positive impact of gender diversity at both workforce and board level for a group affiliated, as well as standalone firms. Originality/value The present study makes a novel contribution to the extant literature on the association between gender diversity and financial performance of firms by examining such diversity at both operational and leadership levels in the context of an emerging country such as India that captures the complex realities pertaining to gender issues. Further, the study contributes to the empirical literature regarding the heterogeneous impact of gender diversity on firm performance in the Indian context.

scholarly journals Overeducation, Overskilling and Mental Well-being

2016 ◽  
Vol 16 (4) ◽  
Author(s):  
Rong Zhu ◽  
Linfeng Chen
Keyword(s):  
Panel Data ◽  
Quantile Regression ◽  
Regression Model ◽  
Dynamic Analysis ◽  
Fixed Effects ◽  
Well Being ◽  
Negative Effects ◽  
Quantile Regression Model ◽  
One Year ◽  
Mental Well Being

Abstract This paper estimates the effects of overeducation and overskilling on mental well-being in Australia. Using fixed-effects (FE) panel estimations, our analysis shows that overeducation does not significantly affect people’s mental well-being. However, overskilling has strong detrimental consequences for mental well-being. Using a panel data quantile regression model with FE, we show that the negative effects of overskilling are highly heterogeneous, with larger impact at the lower end of the distribution of mental well-being. Furthermore, our dynamic analysis shows that the damaging effects of overskilling are transitory, and we find evidence of complete mental well-being adaptation one year after becoming overskilled.

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