中国经济发展的区域不平衡及收敛性分析

文章利用2010—2019年我国31个省份的人均GDP面板数据,使用Dagum基尼系数对经济发展区域不平衡和其来源以及东、中、西三大地区内部和地区间不平衡进行测度,并进一步利用σ收敛以及考虑空间效应的绝对β收敛进行检验。结果表明：经济发展总体不平衡趋于缩小,东部地区经济发展最不平衡,中部地区不平衡性不断扩大,西部地区不平衡性不断缩小且西部地区经济发展逐渐赶超中部地区。经济发展的不平衡来源中区域间不平衡的贡献最大,其中,东西部以及东中部地区间不平衡远大于中西部地区。经济发展水平存在σ收敛,2018年以后东部地区经济发展水平有σ收敛趋势,西部地区存在σ收敛,中部地区呈σ发散;全国及东部和西部地区经济发展水平的绝对β收敛均显著,中部地区不存在绝对β收敛。     

陕西省耕地全要素生产率的区域差异及其收敛性分析

目前,学术界对于耕地生产效率的研究主要侧重于从空间维度对研究区域在同一目标时期内的效率进行分析。鉴此,在采用Malmquist指数法从时间维度对陕西省主要区域耕地跨时期的效率变化进行分析的同时,从σ收敛、绝对β收敛、条件β收敛三方面分别对其收敛性进行分析。通过对陕西省1990—2011年期间耕地全要素生产率的区域差异及其收敛性实证分析发现:从耕地全要素生产率增长的差异角度看,全要素生产率增长的地区差异较为显著,且地区全要素生产率的增长结构也不完全相同;从收敛性角度看,陕西省耕地全要素生产率不仅存在σ收敛,且存在绝对β收敛和条件β收敛,各地区之间耕地全要素生产率水平随着时间的推移将趋于稳定在同一水平。研究结论显示:陕西省耕地全要素生产率在1990——2011年期间具有较大的波动性,省内各地区间存在较大差异,耕地全要素生产率既存在σ收敛,同时也存在绝对β收敛和条件β收敛。建议陕西省通过加强农田基础设施建设和农业科技推广以及改造陕南坡耕地等措施,以保持耕地全要素生产率的持续增长。     

服务型制造行业全要素生产率收敛性的统计检验

文章基于2006-2015年我国服务型制造业的面板数据,运用σ收敛和β收敛检验,从时期、产业和地区三个角度对服务型制造业全要素生产率的变化趋势进行了收敛性检验,并通过建立动态面板数据回归模型分析了影响TFP变动的因素.研究发现:服务型制造业的TFP指数、技术效率指数呈绝对β收敛趋势,但技术进步指数具有绝对β发散性;细分产业间的绝对β收敛性明显;服务型制造业在区域上具有明显的俱乐部趋同性,东、中、西部地区内部的绝对β收敛速度均明显高于总体水平;条件趋同研究表明,行业规模、盈利状况、研发投入、人力资本水平、政府支持、市场化程度、对外开放度等是形成服务型制造业TFP行业差异的主要原因,但影响力度各不相同.     

中国绿色扶贫性增长测度及分析——兼论可持续性减贫

基于OECD绿色增长战略框架,重塑测度中国绿色扶贫性增长指标体系;利用包含多种非期望产出的非径向ZSG-DEA模型和Malmquist指数及其指数分解对1998—2016年中国绿色扶贫性增长进行科学合理的测度,并运用σ收敛、绝对β收敛和条件β收敛对其进行趋势分析。结果发现:中国绿色扶贫性增长水平总体呈现逐年波动下降的趋势,其中两个重要源泉是纯技术进步和效率改进,前者呈现上升趋势,后者呈现下降趋势,且前者的上升幅度低于后者的下降幅度;各地区绿色扶贫性增长水平、纯技术进步以及效率改进存在明显差异,东部地区绿色扶贫性增长水平和效率改进水平最高,中部地区纯技术效率水平最高,而西部地区三个方面指标都最低。进一步分析显示,中国绿色扶贫性增长指数具有收敛性,即存在阶段式σ收敛以及显著的绝对β收敛和条件β收敛。     

数字经济对区域经济增长及其收敛性的影响

文章利用我国2003—2018年30个省份的数据,通过建立空间面板数据模型,研究数字经济对我国区域经济增长及其收敛性的影响。结果表明：我国区域经济存在绝对β收敛和条件β收敛;数字经济发展对区域经济条件β收敛具有正向作用,且对经济增长来说,数字经济发展速度的快慢相对于数字经济发展水平的高低更重要,一个地区的数字经济发展水平和发展速度均对其他地区的经济增长具有正向溢出效应,且对本地区产生正向反馈效应,这为经济发展水平落后的地区实现“弯道超车”提供参考。     

农业高质量发展的空间差异与收敛特征——基于八大综合经济区的实证

文章构建了农业高质量发展评价体系,并利用2011—2018年中国31个省份的面板数据,采用熵权法刻画八大综合经济区的农业高质量发展综合水平。在此基础上,通过Dagum基尼系数以及三维Kernel密度估计对其空间差异展开分析,并通过变异系数法与空间SDM模型考察其σ收敛、空间绝对β收敛和空间条件β收敛特征。结果表明:(1)八大地区农业高质量发展水平均呈现逐步提升特征,但仍存在着地区差异,且区间差异是造成总差异的重要来源。(2)八大地区农业高质量发展具有梯度特征,分化现象开始出现。(3)八大地区农业高质量发展具有σ收敛、绝对β收敛以及条件β收敛特征。     

长江经济带数字普惠金融发展的区域差异、动态演进与收敛性考察

文章基于2011—2020年长江经济带108个城市的面板数据和北京大学数字普惠金融指数,采用Dagum基尼系数分解方法、核密度估计、马尔科夫链分析、空间计量模型等方法,考察长江经济带数字普惠金融水平的区域差异、动态演进与收敛性。研究发现：在样本期内,长江经济带整体和上、中、下游地区的数字普惠金融水平都呈现逐年上升趋势;长江经济带数字普惠金融水平分布具有明显的空间异质性,其中区域间差异是总体差异的主要来源,且区域差异在逐年缩小;长江经济带数字普惠金融各水平状态未来发生向上或向下转移的概率较小,稳定性较强;长江经济带数字普惠金融水平具有显著的正向空间集聚效应,整体和上、中、下游地区的数字普惠金融水平均存在σ收敛、绝对β收敛和条件β收敛。     

中国海洋经济绿色发展水平测度及收敛性分析

海洋经济绿色发展是中国构建新发展格局、建设海洋强国的必然要求。在重新梳理海洋经济绿色发展内涵基础上,首先构建包括海洋经济效益、海洋资源利用、海洋生态环境、海洋创新能力、海洋社会效益和政府支持度六个子系统的海洋经济绿色发展水平评价指标体系,利用CRITIC-熵值组合权重的TOPSIS模型对2010—2020年中国11个沿海省份的海洋经济绿色发展水平进行测度;其次,采用Dagum基尼系数法、收敛模型分析了海洋经济绿色发展水平的区域差异和收敛特征。研究表明：海洋经济绿色发展水平以及六个子系统水平整体呈波动上升趋势;海洋经济绿色发展水平区域总体差异、区域内(间)差异均呈扩大态势,南海地区内部差异最大,北海和南海地区的区域间差异最为明显,超密度贡献率是其区域差异的主要来源;海洋经济绿色发展水平没有表现出σ收敛趋势,全国及三大沿海区域存在显著的绝对β收敛和条件β收敛,海洋产业结构对全国、北海、南海地区海洋经济绿色发展水平的增长率有显著促进作用。     

中国省域数字经济的发展水平与空间收敛性分析

科学把握各地区数字经济的发展水平及其禀赋差异，对于推动中国经济结构提质转型，实现数字中国建设目标具有重要意义。基于此，依据数字经济的内涵特征以及地区数字经济的发展实践，从数字基础、数字产业、数字融合和数字环境四个维度构建指标体系，使用改进的指数法测度并评估中国各省份的数字经济发展水平，进而采用聚类分析法揭示其空间发展格局。研究发现，2015—2020年，中国各省份的数字经济发展水平均呈现增长趋势。广东的数字经济发展水平最高，青海暂居末位，并且落后型省份在数字经济测度的各项维度指标中均不具备优势。从整体来看，中国数字经济发展基本形成了东部省份集体领先、中部群体次之、东北和西部区域相对落后的俱乐部式阶梯递减的空间分布特征。但在发展速度上，省域数字经济的年均增速高达10%以上，并且中西部地区的增速已经明显快于东部地区，形成集体追赶趋势。使用空间面板误差模型对省域数字经济的发展趋势做进一步分析显示，长期内，各地区数字经济发展通过省际之间的经济交流及空间溢出效应而形成收敛趋同现象，这一结论在条件收敛和绝对收敛模型以及变换空间权重矩阵的设定后均成立。研究结果表明，为构建区域数字经济动态互促的良性发...     

数字经济发展对创业活跃度的空间溢出效应研究

数字经济作为引领经济发展的新引擎,其数字化、平台化等特性打破了传统经济发展的区域和空间障碍,对区域创业活跃度产生了深远影响。选取2011—2019年省际面板数据,从数字经济科技创新、数字经济基础建设、数字化人才培养、数字化产业发展及数字普惠金融五个维度测度数字经济综合发展水平,运用空间杜宾模型实证检验数字经济对创业活跃度的影响。研究的主要结论有：数字经济的发展呈现出显著的空间异质性,东部地区数字经济发展速度较快,中部与西部地区发展相对较缓;数字经济对创业活跃度存在空间溢出效应,主要表现在正向的直接效应和总效应两个方面;数字经济五个维度变量对创业活跃度影响的空间效应性质存在差异,数字经济基础设施建设及数字化产业发展对创业活跃度存在空间溢出效应,数字经济科技创新对创业活跃度存在空间虹吸效应,数字化人才培养及数字普惠金融对创业活跃度的空间溢出效应不显著。研究认为应加大中西部地区数字经济的发展力度,平衡各地区数字经济的发展水平,加快数字化产业转型,释放数字红利;推进数字新基建的建设力度,合理配置各地区数字经济资源;推进传统产业与数字技术的深度融合,搭建新型就业平台,促进创业活跃度。     

Local influence diagnostics for generalized linear mixed models with overdispersion

Since the seminal paper by Cook and Weisberg [9 R.D. Cook and S. Weisberg, Residuals and Influence in Regression, Chapman &; Hall, London, 1982. [Google Scholar]], local influence, next to case deletion, has gained popularity as a tool to detect influential subjects and measurements for a variety of statistical models. For the linear mixed model the approach leads to easily interpretable and computationally convenient expressions, not only highlighting influential subjects, but also which aspect of their profile leads to undue influence on the model's fit [17 E. Lesaffre and G. Verbeke, Local influence in linear mixed models, Biometrics 54 (1998), pp. 570–582. doi: 10.2307/3109764[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]]. Ouwens et al. [24 M.J.N.M. Ouwens, F.E.S. Tan, and M.P.F. Berger, Local influence to detect influential data structures for generalized linear mixed models, Biometrics 57 (2001), pp. 1166–1172. doi: 10.1111/j.0006-341X.2001.01166.x[Crossref], [PubMed], [Web of Science ®] , [Google Scholar]] applied the method to the Poisson-normal generalized linear mixed model (GLMM). Given the model's nonlinear structure, these authors did not derive interpretable components but rather focused on a graphical depiction of influence. In this paper, we consider GLMMs for binary, count, and time-to-event data, with the additional feature of accommodating overdispersion whenever necessary. For each situation, three approaches are considered, based on: (1) purely numerical derivations; (2) using a closed-form expression of the marginal likelihood function; and (3) using an integral representation of this likelihood. Unlike when case deletion is used, this leads to interpretable components, allowing not only to identify influential subjects, but also to study the cause thereof. The methodology is illustrated in case studies that range over the three data types mentioned.     

基于图方法的MA和ARMA模型系数检验

应用图模型方法来讨论传统的MA和ARMA模型,证明了MA和ARMA模型的系数为去掉其他时间序列分量线性效应的条件下的偏相关系数,且利用图模型推断算法提出了一种新的参数估计和检验方法。     

An extended random-effects approach to modeling repeated, overdispersed count data

Non-Gaussian outcomes are often modeled using members of the so-called exponential family. The Poisson model for count data falls within this tradition. The family in general, and the Poisson model in particular, are at the same time convenient since mathematically elegant, but in need of extension since often somewhat restrictive. Two of the main rationales for existing extensions are (1) the occurrence of overdispersion, in the sense that the variability in the data is not adequately captured by the model's prescribed mean-variance link, and (2) the accommodation of data hierarchies owing to, for example, repeatedly measuring the outcome on the same subject, recording information from various members of the same family, etc. There is a variety of overdispersion models for count data, such as, for example, the negative-binomial model. Hierarchies are often accommodated through the inclusion of subject-specific, random effects. Though not always, one conventionally assumes such random effects to be normally distributed. While both of these issues may occur simultaneously, models accommodating them at once are less than common. This paper proposes a generalized linear model, accommodating overdispersion and clustering through two separate sets of random effects, of gamma and normal type, respectively. This is in line with the proposal by Booth et al. (Stat Model 3:179-181, 2003). The model extends both classical overdispersion models for count data (Breslow, Appl Stat 33:38-44, 1984), in particular the negative binomial model, as well as the generalized linear mixed model (Breslow and Clayton, J Am Stat Assoc 88:9-25, 1993). Apart from model formulation, we briefly discuss several estimation options, and then settle for maximum likelihood estimation with both fully analytic integration as well as hybrid between analytic and numerical integration. The latter is implemented in the SAS procedure NLMIXED. The methodology is applied to data from a study in epileptic seizures.     

Determining the number of factors with potentially strong within-block correlations in error terms

ABSTRACT

We develop methods to estimate the number of factors when error terms have potentially strong correlations in the cross-sectional dimension. The information criteria proposed by Bai and Ng (2002 Bai, J., Ng, S. (2002). Determining the number of factors in approximate factor models. Econometrica 70:191–221.[Crossref], [Web of Science ®] , [Google Scholar]) require the cross-sectional correlations between the error terms to be weak. Violation of this weak correlation assumption may lead to inconsistent estimates of the number of factors. We establish two data-dependent estimators that are consistent whether the error terms are weakly or strongly correlated in the cross-sectional dimension. To handle potentially strong cross-sectional correlations between the error terms, we use a block structure in which the within-block correlation may either be weak or strong, but the between-block correlation is limited. Our estimators allow imperfect knowledge and a moderate misspecification of the block structure. Monte-Carlo simulation results show that our estimators perform similarly to existing methods for cases in which the conventional weak correlation assumption is satisfied. When the error terms have a strong cross-sectional correlation, our estimators outperform the existing methods.     

A semiparametric extended hazard regression model with time-dependent covariates

We introduce a general class of semiparametric hazard regression models, called extended hazard (EH) models, that are designed to accommodate various survival schemes with time-dependent covariates. The EH model contains both the Cox model and the accelerated failure time (AFT) model as its subclasses so that we can use this nested structure to perform model selection between the Cox model and the AFT model. A class of estimating equations using counting process and martingale techniques is developed to estimate the regression parameters of the proposed model. The performance of the estimating procedure and the impact of model misspecification are assessed through simulation studies. Two data examples, Stanford heart transplant data and Mediterranean fruit flies, egg-laying data, are used to demonstrate the usefulness of the EH model.     

Statistical analysis of controlled calibration model with replicates

In this work, we generalize the controlled calibration model by assuming replication on both variables. Likelihood-based methodology is used to estimate the model parameters and the Fisher information matrix is used to construct confidence intervals for the unknown value of the regressor variable. Further, we study the local influence diagnostic method which is based on the conditional expectation of the complete-data log-likelihood function related to the EM algorithm. Some useful perturbation schemes are discussed. A simulation study is carried out to assess the effect of the measurement error on the estimation of the parameter of interest. This new approach is illustrated with a real data set.     

空间计量经济学理论体系的解析及其展望

随着空间经济理论研究出现重大突破,空间计量经济学也从边缘走向现代计量经济学的主流。重点对空间计量经济模型的设立包括空间横截面数据模型、空间面板数据模型以及空间离散数据模型进行讨论,对模型参数估计方法包括最大似然估计法、两阶段最小二乘法和矩估计法等进行分析,对模型检验方法包括Moran’s I方法和LM/RS方法等内容进行总结,最后展望了该理论研究未来的发展趋势。     

Comparison of naïve,Kenward–Roger,and parametric bootstrap interval approaches to small-sample inference in linear mixed models

In mixed models the mean square error (MSE) of empirical best linear unbiased estimators generally cannot be written in closed form. Unlike traditional methods of inference, parametric bootstrapping does not require approximation of this MSE or the test statistic distribution. Data were simulated to compare coverage rates for intervals based on the naïve MSE approximation and the method of Kenward and Roger, and parametric bootstrap intervals (Efron's percentile, Hall's percentile, bootstrap-t). The Kenward–Roger method performed best and the bootstrap-t almost as well. Intervals were also compared for a small set of real data. Implications for minimum sample size are discussed.     

计量经济学和统计学视角下的线性回归模型——再议线性回归模型的设定

从属性、构建方法及意义等方面,分析研究线性回归模型在计量经济学和统计学两学科视角下的差异,并根据这种差异进一步提出回归模型的基本设定思路。研究表明:识别这种差异是完成模型设定工作的基础性和必要性举措,有助于实现线性回归模型的正确设定。以经典例证对计量经济学和统计学回归模型在应用中的区别以及模型设定问题进行进一步展示和分析。     

Models Comparison for Step-Stress Accelerated Life Testing

In this article, four basic models for step-stress accelerated life testing are introduced and compared: cumulative exposure model (CEM), linear cumulative exposure model (LCEM), tampered random variable model (TRVM), and tampered failure rate model (TFRM). Limitations of the four models are also introduced for better use of the models.