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

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.

Comparison of Bayesian quantile and frequency-oriented regressions in studying the trend of discharge changes in several hydrometric stations of Gorganroud basin in Iran.

Climate change causes fluctuations in temperature and precipitation. As a result, it affects the discharge of rivers, the most important consequence of which is the tendency toward extreme events such as torrential rains and widespread droughts. River discharge is one of the most important climatic and hydrological parameters. Investigating the changes in this parameter is one of the main prerequisites in the management and proper use of water resources and rivers. Most trend detection studies are based on analyzing changes in the mean or middle of the data. They do not provide information on how changes occur in different data ranges. Therefore, to investigate parameter changes in a different range of the data series, various regression models were proposed. Frequentist quantile regression and Bayesian quantile regression models were used to estimate their trend and trend slope in different quantiles of discharge in different seasons of the year for Arazkouseh, Tamar, and Galikesh stations of Gorganroud basin in northern Iran with the statistical period of 1346–1396 (1966–2016). The results show that in most seasons of the year, high discharge rates for all 3 stations have decreased with a steep slope, and only in summer, Tamar and Galikesh stations have had an increasing trend, but low discharge rates have not changed significantly. Spatially, the discharge values at Arazkouseh station have a decreasing trend with a higher slope rate, and in terms of time, the most decreasing trend has been in spring. Comparing the models also shows that the Bayesian quantile regression model provides more accurate and reliable results than the frequency-oriented quantile regression model. In general, quantile regression models are useful for predicting and estimating extreme high and low discharge changes for better management to reduce flood and drought damage.

Minimum distance quantile regression for spatial autoregressive panel data models with fixed effects

This paper considers the quantile regression model with individual fixed effects for spatial panel data. Efficient minimum distance quantile regression estimators based on instrumental variable (IV) method are proposed for parameter estimation. The proposed estimator is computational fast compared with the IV-FEQR estimator proposed by Dai et al. (2020). Asymptotic properties of the proposed estimators are also established. Simulations are conducted to study the performance of the proposed method. Finally, we illustrate our methodologies using a cigarettes demand data set.

Resource Opportunity in China’s Market Transition and Governance: Time Factor in Urban Housing Inequality

Since China’s reform and opening up, the country’s rapid marketization process has been accompanied by the rapid growth of inequality, which has been significant for all classes of society. In terms of its impact, housing inequality is particularly noticeable. In this paper, we discuss the influence of real-estate purchase time, organization, human capital, and political capital on the value of real estate and the appreciation of real estate in China by using a conditional mean model and a quantile regression model. The differences in the degree of influence of these factors on different quantile levels are also investigated. We found that, after adding the time factor, the prior possession of resources in the early stage of market transformation will benefit the long-term marketization process. Organizations that can penetrate “market-redistribution” and professions that directly participate in the distribution of real-estate resources also have significant advantages in this regard.

Antibody Response and Safety After mRNA-1273 SARS-CoV-2 Vaccination in Peritoneal Dialysis Patients – the Vienna Cohort

BackgroundDialysis patients are at high risk for a severe clinical course after infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Safety and early immune responses after mRNA-based vaccination have been reported mostly in patients on hemodialysis (HD), whereas reports of peritoneal dialysis (PD) patients remain rare.MethodsIn this retrospective observational study, 39 PD patients had received two doses of the mRNA-1273 Moderna® vaccine. We analyzed SARS-CoV-2 Spike (S) antibody titers 4 weeks after each dose of mRNA-1273 and report local and systemic side effects in PD patients that occurred within one week after each mRNA-1273 dose. Using a quantile regression model we examined factors that might influence SARS-CoV-2 S antibody levels in PD patients.ResultsFour weeks after the first dose of mRNA-1273 vaccine 33 of 39 (84.6%) PD patients seroconverted and presented with 6.62 U/mL (median; IQR 1.57-22.5) anti-SARS-CoV-2 S antibody titers. After the second dose, 38 of 39 (97.4%) PD patients developed anti-SARS-CoV-2 S antibodies and titers increased significantly (median 968 U/mL; IQR 422.5-2500). Pain at the injection site was the most common local adverse event (AE) (71%). Systemic AEs occurring after the first dose were mostly fatigue (33%) and headache (20%). No severe systemic AEs were reported after the first injection. After the second dose the incidence and the severity of the systemic AEs increased. The most common systemic AEs were: fatigue (40.5%), headache (22.5%), joint pain (20%), myalgia (17.5%) and fever (13%). Lower Davies Comorbidity Score (p=0.04) and shorter dialysis vintage (p=0.017) were associated with higher antibody titers after the first dose. Patients with higher antibody titers after the first dose tended to have higher antibody titers after the second dose (p=1.53x10-05).ConclusionsPeritoneal dialysis patients in this cohort had a high seroconversion rate of 97.4%, showed high antibody titers after full vaccination and tolerated the anti-SARS-CoV-2 mRNA-1273 vaccine well without serious adverse events.

ON THE IMPACT OF REAL ESTATE PRICES ON THE DEVELOPMENT OF REGIONAL ECONOMY IN CHINA–AN ESTIMATION BASED ON PANEL QUANTILE REGRESSION MODEL

The paper analyzes the mechanism of real estate prices on economic development with panel quantile regression model. It is found that real estate prices can significantly promote economic development. Generally speaking, the contribution of real estate prices to economic development in regions with higher level of economic development is higher than that in regions with lower level. With the continuous improvement of the quantile, the impact of real estate prices has generally increased gradually, and the impact of urbanization level basically shows the law of diminishing marginal effect.