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.

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.

scholarly journals Improving estimations in quantile regression model with autoregressive errors

2018 ◽  
Vol 22 (Suppl. 1) ◽  
pp. 97-107 ◽  
Author(s):  
Bahadır Yuzbasi ◽  
Yasin Asar ◽  
Samil Sik ◽  
Ahmet Demiralp
Keyword(s):  
Quantile Regression ◽  
Regression Model ◽  
Simulation Experiment ◽  
Ordinary Least Squares ◽  
Least Squares Regression ◽  
White Noise Process ◽  
Respiratory Mortality ◽  
Explanatory Variables ◽  
Quantile Regression Model ◽  
Regression Approach

An important issue is that the respiratory mortality may be a result of air pollution which can be measured by the following variables: temperature, relative humidity, carbon monoxide, sulfur dioxide, nitrogen dioxide, hydrocarbons, ozone, and particulates. The usual way is to fit a model using the ordinary least squares regression, which has some assumptions, also known as Gauss-Markov assumptions, on the error term showing white noise process of the regression model. However, in many applications, especially for this example, these assumptions are not satisfied. Therefore, in this study, a quantile regression approach is used to model the respiratory mortality using the mentioned explanatory variables. Moreover, improved estimation techniques such as preliminary testing and shrinkage strategies are also obtained when the errors are autoregressive. A Monte Carlo simulation experiment, including the quantile penalty estimators such as lasso, ridge, and elastic net, is designed to evaluate the performances of the proposed techniques. Finally, the theoretical risks of the listed estimators are given.

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