Two Stage Estimation and Its Covariance Matrix in Multivariate Seemingly Unrelated Regression System

Multivariate seemingly unrelated regression system is raised first and the two stage estimation and its covariance matrix are given. The results of the literatures[1-5] are extended in this paper.

Driving factors of CO_(2)emissions in South American countries:An application of Seemingly Unrelated Regression model

Carbon emissions have become a critical concern in the global effort to combat climate change,with each country or region contributing differently based on its economic structures,energy sources,and industrial activities.The factors influencing carbon emissions vary across countries and sectors.This study examined the factors influencing CO_(2)emissions in the 7 South American countries including Argentina,Brazil,Chile,Colombia,Ecuador,Peru,and Venezuela.We used the Seemingly Unrelated Regression(SUR)model to analyse the relationship of CO_(2)emissions with gross domestic product(GDP),renewable energy use,urbanization,industrialization,international tourism,agricultural productivity,and forest area based on data from 2000 to 2022.According to the SUR model,we found that GDP and industrialization had a moderate positive effect on CO_(2)emissions,whereas renewable energy use had a moderate negative effect on CO_(2)emissions.International tourism generally had a positive impact on CO_(2)emissions,while forest area tended to decrease CO_(2)emissions.Different variables had different effects on CO_(2)emissions in the 7 South American countries.In Argentina and Venezuela,GDP,international tourism,and agricultural productivity significantly affected CO_(2)emissions.In Colombia,GDP and international tourism had a negative impact on CO_(2)emissions.In Brazil,CO_(2)emissions were primarily driven by GDP,while in Chile,Ecuador,and Peru,international tourism had a negative effect on CO_(2)emissions.Overall,this study highlights the importance of country-specific strategies for reducing CO_(2)emissions and emphasizes the varying roles of these driving factors in shaping environmental quality in the 7 South American countries.

Bayes and empirical Bayes iteration estimators in two seemingly unrelated regression equations

For a system of two seemingly unrelated regression equations given by (?)(y_1 is an m×1 vector and y_2 is an n×1 vector,m≠n),employ- ing the covariance adjusted technique,we propose the parametric Bayes and empirical Bayes iteration estimator sequences for regression coefficients.We prove that both the covariance matrices converge monotonically and the Bayes iteration estimator squence is consistent as well.Based on the mean square error (MSE) criterion,we elaborate the su- periority of empirical Bayes iteration estimator over the Bayes estimator of single equation when the covariance matrix of errors is unknown.The results obtained in this paper further show the power of the covariance adiusted approach.

A NEW ESTIMATE OF REGRESSION COEFFICIENTS IN SEEMINGLY UNRELATED REGRESSION SYSTEM

For a seemingly unrelated regression system consisting of m equations, the information contained in all the equations is divided into sample information and additional information, and a new estimate of the regression coefficients is proposed by using successive superposition. More precisely, the following three problems are solved: (ⅰ) a statistic summarized the additional information is constructed, (ⅱ) a procedure which superposes the additional information on the sample information and a new estimate of regression coefficients are proposed, (ⅲ) some properties of the new estimate are established.

TWO-STAGE ESTIMATION FOR SEEMINGLY UNRELATED NONPARAMETRIC REGRESSION MODELS

This paper is concerned with the estimating problem of seemingly unrelated （SU） non- parametric regression models. The authors propose a new method to estimate the unknown functions, which is an extension of the two-stage procedure in the longitudinal data framework. The authors show the resulted estimators are asymptotically normal and more efficient than those based on only the individual regression equation. Some simulation studies are given in support of the asymptotic results. A real data from an ongoing environmental epidemiologie study are used to illustrate the proposed procedure.

AN EFFICIENT PROCEDURE FOR ESTIMATING TWO SEEMINGLY UNRELATED REGRESSIONS

I. INTRODUCTIONConsider the following system of two seemingly unrelated regressions: Y_i=X_iβ_i+ε_i,

Existence of the Uniformly Minimum Risk Unbiased Estimator in Seemingly Unrelated Regression System

For a seemingly Unrelated regression system with the assumption of normality,a necessary and sufficient condition for the existence of the Uniformly Minimum Risk Unbiased (UMRU)estimator of regression coefficients under strictly convex loss is obtained;it is proved that any unbiased estimator can not improve the least squares estimator;it is also shown that no UMRU estimator exists under missing observations.

On Estimating Regression Coefficients in Seemingly Unrelated Regression System

In the system of m （m ≥ 2） seemingly unrelated regressions, we show that the Gauss-Markov estimator （GME） of any regression coefficients has unique simplified form, which exactly equals to the one- step covariance-adjusted estimator of the regression coefficients, and hence we conclude that for any finite k ≥ 2 the k-step covariance-adjusted estimator degenerates to the one-step covariance-adjusted estimator and the corresponding two-stage Aitken estimator has exactly one simplified form. Also, the unique simplified expression of the GME is just the estimator presented in the Theorem 1 of Wang＇ work [1988]. A new estimate of regression coefficients in seemingly unrelated regression system, Science in China, Series A 10, 1033-1040].

EFFICIENT ESTIMATION OF SEEMINGLY UNRELATED ADDITIVE NONPARAMETRIC REGRESSION MODELS

This paper is concerned with the estimating problem of seemingly unrelated(SU)nonparametric additive regression models.A polynomial spline based two-stage efficient approach is proposed to estimate the nonparametric components,which takes both of the additive structure and correlation between equations into account.The asymptotic normality of the derived estimators are established.The authors also show they own some advantages,including they are asymptotically more efficient than those based on only the individual regression equation and have an oracle property,which is the asymptotic distribution of each additive component is the same as it would be if the other components were known with certainty.Some simulation studies are conducted to illustrate the finite sample performance of the proposed procedure.Applying the proposed procedure to a real data set is also made.

ITERATIVE QUASI-LIKELIHOOD FOR SEEMINGLY UNRELATED REGRESSION SYSTEMS

In the seemingly unrelated regression systems, the existing quasi-likelihood is always involved in the difficult problem of calculating inverse of a high order matrix specially for large systems. To avoid this problem, the new quasi-likelihood proposed in this paper is based mainly on a linearly iterative process of some unbiased estimating functions.Some finite sample properties and asymptotic behaviours for this new quasi-likelihood are investigated. These results show that the new quasi-likelihood for parameter of interest is E-sufficient, iteratively efficient and approximately efficient. Some examples are given to illustrate the theoretical results.

Efcient Estimation of Varying Coefcient Seemly Unrelated Regression Model

In this paper, we propose a class of varying coefficient seemingly unrelated regression models, in which the errors are correlated across the equations. By applying the series approximation and taking the contemporaneous correlations into account, we propose an efficient generalized least squares series estimation for the unknown coefficient functions. The consistency and asymptotic normality of the resulting estimators are established. In comparison with the ordinary/east squares ones, the proposed estimators are more efficient with smaller asymptotical variances. Some simulgtlon＇studies and a real application are presented to demonstrate the finite sample performance of the proposed methods. In addition, based on a B-spline approximation, we deduce the asymptotic bias and variance of the proposed estimators.

SURS中回归系数GCIE系列的极限问题

讨论了半相依回归系统(SURS)中回归系数的广义协方差改进估计(GCIE)的效率问题,得出了几种估计的效率度量公式在极限意义下等价的结论.以该结论为基础,我们对一些简单的协方差结构的SURS中回归系数的GCIE系列进行计算机模拟,得到了m=3时,在这些模型下,GCIE的极限便是最佳线性无偏估计的结论.

Does digitalization mitigate regional inequalities?Evidence from China

Regional inequality significantly influences sustainable development and human well-being.In China,there exists pronounced regional disparities in economic and digital advancements;however,scant research delves into the interplay between them.By analyzing the economic development and digitalization gaps at regional and city levels in China,extending the original Cobb-Douglas production function,this study aims to evaluate the impact of digitalization on China's regional inequality using seemingly unrelated regression.The results indicate a greater emphasis on digital inequality compared to economic disparity,with variable coefficients of 0.59 for GDP per capita and 0.92 for the digitalization index over the past four years.However,GDP per capita demonstrates higher spatial concentration than digitalization.Notably,both disparities have shown a gradual reduction in recent years.The southeastern region of the Hu Huanyong Line exhibits superior levels and rates of economic and digital advancement in contrast to the northwestern region.While digitalization propels economic growth,it yields a nuanced impact on achieving balanced regional development,encompassing both positive and negative facets.Our study highlights that the marginal utility of advancing digitalization is more pronounced in less developed regions,but only if the government invests in the digital infrastructure and education in these areas.This study's methodology can be utilized for subsequent research,and our findings hold the potential to the government's regional investment and policy-making.

生猪产业规模化发展与碳排放:促进还是抑制?

在使用生命周期法测算2007−2020年省际生猪碳排放的基础上,系统考察了生猪产业规模化发展对生猪碳排放的影响与作用机制。结果表明:第一,生猪碳排放呈现出明显的空间溢出效应和聚集效应,即一个地区的生猪碳排放与周边地区密切相关;第二,生猪产业规模化发展对生猪碳排放呈现显著的“倒U型”关系,规模化发展对生猪碳排放的影响存在显著的门槛效应,当生猪产业规模化率保持在45.1%以上时,规模化发展可有效促进生猪产业碳减排;第三,小规模生猪养殖占比与生猪碳排放是显著的单调递增关系,而中规模和大规模生猪养殖占比与生猪碳排放之间表现显著的“倒U”型关系;第四,聚集效应和技术进步效应是生猪产业规模化发展影响生猪碳排放的主要作用途径。建议合理规划生猪产业的空间布局,发展适度规模养殖,发挥技术进步的推动作用,稳妥实现生猪产业碳减排与规模化发展的“双赢”目标。

Predicting carbon storage of mixed broadleaf forests based on the finite mixture model incorporating stand factors,site quality,and aridity index

The diameter distribution function(DDF)is a crucial tool for accurately predicting stand carbon storage(CS).The current key issue,however,is how to construct a high-precision DDF based on stand factors,site quality,and aridity index to predict stand CS in multi-species mixed forests with complex structures.This study used data from70 survey plots for mixed broadleaf Populus davidiana and Betula platyphylla forests in the Mulan Rangeland State Forest,Hebei Province,China,to construct the DDF based on maximum likelihood estimation and finite mixture model(FMM).Ordinary least squares(OLS),linear seemingly unrelated regression(LSUR),and back propagation neural network(BPNN)were used to investigate the influences of stand factors,site quality,and aridity index on the shape and scale parameters of DDF and predicted stand CS of mixed broadleaf forests.The results showed that FMM accurately described the stand-level diameter distribution of the mixed P.davidiana and B.platyphylla forests;whereas the Weibull function constructed by MLE was more accurate in describing species-level diameter distribution.The combined variable of quadratic mean diameter(Dq),stand basal area(BA),and site quality improved the accuracy of the shape parameter models of FMM;the combined variable of Dq,BA,and De Martonne aridity index improved the accuracy of the scale parameter models.Compared to OLS and LSUR,the BPNN had higher accuracy in the re-parameterization process of FMM.OLS,LSUR,and BPNN overestimated the CS of P.davidiana but underestimated the CS of B.platyphylla in the large diameter classes(DBH≥18 cm).BPNN accurately estimated stand-and species-level CS,but it was more suitable for estimating stand-level CS compared to species-level CS,thereby providing a scientific basis for the optimization of stand structure and assessment of carbon sequestration capacity in mixed broadleaf forests.

生态补偿扶贫背景下农户生计资本影响因素研究

随着"生态补偿脱贫一批"的提出,生态补偿被赋予了扶贫解困的新使命。量化研究生态补偿对农户生计资本的影响,有利于揭示现有生态补偿政策的实际扶贫效果。基于英国国际发展部提出的可持续生计框架,构建测度指标测算了贵州省3个贫困县432个农户样本的生计资本,通过似不相关估计分析农户生计资本的影响因素。结果表明:参与生态补偿政策农户的总体生计资本水平低于未参与政策农户,其中参与政策农户的自然资本高于未参与政策农户,人力资本、物质资本和社会资本低于未参与政策农户。不同类型的补偿方式对农户生计资本的影响差异明显,现金型直接补偿侧重于增加农户用于发展的物质条件、改善农户生产生活的脆弱性背景,而公益岗位型间接补偿则有利于提升农户的内生发展能力,两者在促进农户的生计资本建设上具有一定的互补性。为此,在贫困地区需要进一步加大生态补偿的公共财政投入,并充分发挥不同补偿项目的协同作用,让农户真正从生态保护中获益。

落叶松人工林直径分布动态预估模型

基于小兴安岭地区和长白山地区102块落叶松人工纯林固定标准地复测数据（10 a间隔期）,采用参数预测模型（PPM）系统,建立了前期Weibull分布参数（b1）与前期林分调查因子模型、前期参数c1与b1之间的回归模型、两期参数b2与b1的回归模型、以及两期参数c2与b2之间的回归模型,并采用似乎不相关回归（SUR）理论,估计了模型的参数;利用＂刀切法＂选择平均相对误差（ME）、平均相对误差绝对值（MAE）、预测精度（P）、相对误差（B）、误差指数（IE）等指标,分别对所建立的参数动态预测方程及直径分布动态预测结果进行了检验。结果表明：所有模型的R2a较好（0.428~0.897）,RMSE均较小（0.37~0.94）,所建立的直径分布动态预测模型具有较好的拟合效果。通过检验,所建立的参数动态模型预估能力较好（-10%95%）,并能较好地预测落叶松人工林未来直径分布（B0=4.38%,B1=12.38%,IE=524）。

消费者对药品的感知风险与风险态度研究

消费者对药品的感知风险与风险态度之间存在显著负相关关系。在感知风险方面,消费者对药品的感知风险与对毒胶囊事件说法的认识、宗教信仰以及相关信息搜索具有显著正相关关系,与相关部门处理措施的满意度和专业背景具有显著负相关关系;在风险态度方面,消费者对药品的风险态度与其宗教信仰、体检频率具有显著正相关关系,与个人卫生习惯、医疗保险具有显著负相关关系。因此,在药品危机恢复阶段,管理部门、行业协会、药品生产者应采取积极有效的措施进行善后处理,重塑公众信任,指导消费者安全用药、合理用药;强化媒体作为主要的社会监督力量在药品安全事件中的作用;消费者应客观、科学地认识药品安全事件,避免陷入恐慌。

影响农村消费的收入结构效应分析

利用可同时讨论多个相互关联模型的似不相关回归(SUR)分析方法,以2000年-2006年我国31省市农村居民收入与消费的面板数据为研究对象,比较分析了工资性收入、家庭经营性收入、财产性收入以及转移性收入对农村居民7大类生活消费支出的影响.回归结果表明,工资性收入对农民住房支出和文教娱乐用品及服务支出影响显著;家庭经营性收入对农民食品、衣着、交通、通讯和医疗保健支出作用明显;转移性收入主要影响农民家庭设备用品及服务的消费支出.

基于定期调查数据的全林分年生长预测模型研究

以北京山区油松为研究对象,利用可变生长率法构造油松全林分年生长预测模型,并通过似乎不相关联立估计全林分生长模型的参数。研究结果表明:利用可变生长率法建立全林分年生长预测模型符合林分发展的规律,解决了林分生长预测的阶段无偏性,同时为林分经营者提供了林分年变化量,而且通过似乎不相关联立估计得到的全林分生长模型参数,没有明显的估计偏差,从而提高了参数估计的有效性。