Quasi Maximum Likelihood for MESS Varying Coefficient Panel Data Models with Fixed Effects

The study of spatial econometrics has developed rapidly and has found wide applications in many different scientific fields,such as demog­raphy,epidemiology,regional economics,and psychology.With the deepening of research,some scholars find that there are some model specifications in spatial econometrics,such as spatial autoregressive(SAR)model and matrix exponential spatial specification(MESS),which cannot be nested within each other.Compared with the common SAR models,the MESS models have computational advantages because it eliminates the need for logarithmic determinant calculation in maxi­mum likelihood estimation and Bayesian estimation.Meanwhile,MESS models have theoretical advantages.However,the theoretical research and application of MESS models have not been promoted vigorously.Therefore,the study of MESS model theory has practical significance.This paper studies the quasi maximum likelihood estimation for ma­trix exponential spatial specification(MESS)varying coefficient panel data models with fixed effects.It is shown that the estimators of model parameters and function coefficients satisfy the consistency and asymp­totic normality to make a further supplement for the theoretical study of MESS model.

Bootstrap inference of the skew-normal two-way classification random effects model with interaction

In this paper,we consider the statistical inference problems for the fixed effect and variance component functions in the two-way classification random effects model with skewnormal errors.Firstly,the exact test statistic for the fixed effect is constructed.Secondly,using the Bootstrap approach and generalized approach,the one-sided hypothesis testing and interval estimation problems for the single variance component,the sum and ratio of variance components are discussed respectively.Further,the Monte Carlo simulation results indicate that the exact test statistic performs well in the one-sided hypothesis testing problem for the fixed effect.And the Bootstrap approach is better than the generalized approach in the one-sided hypothesis testing problems for variance component functions in most cases.Finally,the above approaches are applied to the real data examples of the consumer price index and value-added index of three industries to verify their rationality and effectiveness.

INFLUENCE ANALYSIS IN NONLINEAR MODELS WITH RANDOM EFFECTS

In this paper,a unified diagnostic method for the nonlinear models with random effects based upon the joint likelihood given by Robinson in 1991 is presented.It is shown that the case deletion model is equivalent to the mean shift outlier model.From this point of view,several diagnostic measures,such as Cook distance,score statistics are derived.The local influence measure of Cook is also presented. A numerical example illustrates that the method is available.

INFLUENCE ANALYSIS ON EXPONENTIAL NONLINEAR MODELS WITH RANDOM EFFECTS

This paper presents a unified diagnostic method for exponential nonlinear models with random effects based upon the joint likelihood given by Robinson in 1991. The authors show that the case deletion model is equivalent to mean shift outlier model. From this point of view, several diagnostic measures, such as Cook distance, score statistics are derived. The local influence measure of Cook is also presented. Numerical example illustrates that our method is available.

Evaluation of Economic, Social Effects of Renewable Energy Technologies

What is sustainability? Does it only concern the environment or even socio-economic policies? It is only a question of ethics or a redefinition of industrial policy oriented towards the use of renewable energy, it can bring benefits both atmospheric and social employment. The need for the development of renewable sources can be in tune with the correct management of the territory in consideration of the fact that these sources involve the widespread implementation of small and medium-sized plants. A model of economic development based on renewable sources should respect the peculiarities and characteristics of the territories involved. It should also think of the territory as a “value” to be strengthened and used in a sustainable and integrated way and no longer as a passive platform on which to install plants. Solar thermal and photovoltaic, biomass, geothermal, hydrological, wind power are some of the sources the various countries must constantly invest. This publication is based on these concepts starting from an analysis of the employment data of the OECD “Organisation for Economic Co-operation and Development countries”, comparing them successively with the results of renewable energy productivity. The analysis was performed by analyzing a sample of 22 countries over a period of 20 years, after which the regression curve for the variables with the OLS method was created. This econometric method has allowed us to analyze the impact that renewable technologies have on the parameters of social welfare and in particular on unemployment.

Variable Selection for Fixed Effects Varying Coefficient Models

We consider the problem of variable selection for the fixed effects varying coefficient models. A variable selection procedure is developed using basis function approximations and group nonconcave penalized functions, and the fixed effects are removed using the proper weight matrices. The proposed procedure simultaneously removes the fixed individual effects, selects the significant variables and estimates the nonzero coefficient functions. With appropriate selection of the tuning parameters, an asymptotic theory for the resulting estimates is established under suitable conditions. Simulation studies are carried out to assess the performance of our proposed method, and a real data set is analyzed for further illustration.

ESTIMATION IN A SEMI-VARYING COEFFICIENT MODEL FOR PANEL DATA WITH FIXED EFFECTS

This paper considers a semi-varying coefficient model for panel data with fixed effects,proposes the profile-likelihood-based estimators for the parametric and nonparametric components,and establishes convergence rates and asymptotic normality properties for both estimators.Simulation results show that the proposed estimators behave well in finite sample cases.

数字普惠金融对企业数字化转型的影响——基于A股上市公司的研究

数字普惠金融对企业数字化发挥引导和促转作用。本文将数字普惠金融2013~2023年市级数据与中国上市企业数据进行匹配,深入研究数字普惠金融对企业数字化转型的影响。研究发现:数字普惠金融的发展对企业数字化转型有显著驱动作用,二者之间的非线性影响存在门槛效应,这种作用在非国有企业、小型企业和高科技企业中更为明显。进一步研究二者之间的传导机制,发现数字普惠金融通过3个渠道助力企业数字化转型:缓解融资约束、优化金融错配、加大研发投入。据此,本文提出了数字普惠金融服务实体经济促进企业数字化转型的政策建议。

黄河流域碳排放脱钩与能源安全关系研究

为了给全面推进黄河流域高质量发展和实现“双碳”目标等提供参考,以2005—2022年为研究期,构建能源安全指标体系,采用熵值法测度黄河流域各省份能源安全水平,采用Tapio模型测度黄河流域各省份碳排放脱钩状态,采用固定效应模型对碳排放脱钩与能源安全的关系进行实证分析,结果表明:1)黄河流域各省份能源安全水平在研究期均呈上升趋势,但研究期末能源安全压力依然较大且省份间差别较大,能源安全形势整体上不容乐观;2)黄河流域各省份碳排放脱钩状态在研究期向好发展,多数省份曾呈现强脱钩状态,但脱钩状态不稳定即难以保持最佳状态;3)能源安全水平的提高对黄河流域碳排放脱钩具有促进效应,这种促进效应在2012年以后更加显著。提出了系统化提高黄河流域能源安全水平、加快“碳达峰”进程和加快转变经济发展方式等政策建议。

湖南省农田水利设施治理效率及影响因素研究

农田水利设施治理效率与农村经济高质量发展密切相关,在保障人民群众切身利益和农业可持续发展上发挥着重要的作用。选取2012-2021年湖南省14个市(州)的面板数据,采用三阶段SBM与Malmquist指数相结合的方法,从静态与动态两个视角对农田水利设施的治理效率进行分析,并在此基础上运用固定效应模型,进一步探讨治理效率的影响因素。研究表明:在剔除环境因素与随机误差后,农民专业合作社、农户人均收入、地区生产总值和城市化水平对农田水利设施治理效率有积极性的影响,过量的堤防建设会弱化农田水利设施治理效率;调整后湖南省的农田水利治理效率趋于稳定,纯技术效率和规模效率则有不同程度的增长;2012-2021年湖南省农田水利设施的全要素生产率呈现“U型”波动增长,长株潭地区全要素生产率的增长最快,湘南地区增速最慢,出现极端值的原因主要是技术进步变化指数的变动;农业种植结构、农村居民文盲率、经济发展水平和财政分权度对农田水利治理效率有负面影响。

数字物流赋能农村居民消费升级:理论基础、机制分析和实现路径

我国农村地区人口众多,消费市场潜力巨大,其消费市场的扩大将促进国内供给体系升级和转型。数字物流作为连接生产与消费的关键纽带,不仅提升了农村居民的消费品可得性,还促进了农产品的上行发展,对推动农村居民消费、释放我国内需潜力具有十分重要的现实意义。以我国2010—2021年278个地级市和4个直辖市的面板数据为基础,通过建立面板固定效应模型、中介效应模型、门槛效应模型,实证检验数字物流对农村居民消费升级的影响及作用机制。研究发现:数字物流对农村居民消费升级具有正向影响;产业结构升级在数字物流推动农村居民消费升级过程中发挥着部分中介效应;同时,产业结构升级在数字物流推动农村居民消费升级的过程中发挥着门槛效应;数字物流对农村居民消费升级的影响存在不同区域、不同发展阶段、数字物流不同维度、不同消费升级水平的异质性。

Bootstrap LM Tests for Spatial Dependence in Panel Data Models with Fixed Effects

This paper applies bootstrap methods to LM tests(including LM-lag test and LM-error test) for spatial dependence in panel data models with fixed effects, and removes fixed effects based on orthogonal transformation method proposed by Lee and Yu(2010). The consistencies of LM tests and their bootstrap versions are proved, and then some asymptotic refinements of bootstrap LM tests are obtained. It shows that the convergence rate of bootstrap LM tests is O((N T)-2) and that of fast double bootstrap LM tests is O((NT)-5/2). Extensive Monte Carlo experiments suggest that,compared to aysmptotic LM tests, the size of bootstrap LM tests gets closer to the nominal level of signifiance, and the power of bootstrap LM tests is higher, especially in the cases with small spatial correlation. Moreover, when the error is not normal or with heteroskedastic, asymptotic LM tests suffer from severe size distortion, but the size of bootstrap LM tests is close to the nominal significance level.Bootstrap LM tests are superior to aysmptotic LM tests in terms of size and power.

Modelling energy efficiency in China:a fixed-effects panel stochastic frontier approach

This paper combines energy demand modelling with stochastic frontier analysis to investigate the changing trends,variations and determinants of energy efficiency for 27 Chinese provinces over the period 1995 to 2014.An aggregate‘frontier’energy demand function and an efficiency function are estimated simultaneously.We obtained several findings.First,the energy intensity is not a particularly good indicator of energy efficiency.Second,the energy efficiency levels for all the provinces improved during the sample period,but the current efficiency levels are still low,implying great potential for energy saving.In addition,the energy efficiency gap among the provinces seems to have widened over the past 20 years,as the variance has increased by almost three times.Finally,technological progress driven by new investment and the development of market mechanisms are two important drivers of energy efficiency improvement.

湖南省农村产业融合对农民增收的影响

推进农民持续稳定增收是实施乡村振兴战略的核心任务,农村产业融合作为推动农民增收的有效途径,受到了学界和业界的广泛关注。本研究选取湖南省作为研究对象,运用2013—2022年的面板数据,采用熵值法对各地区农村产业融合水平进行测算,并通过固定效应模型来探讨农村产业融合与农民人均可支配收入之间的关系。研究结果表明:湖南省农村产业融合程度总体上呈现上升趋势,但区域间的差异依然显著;农村产业融合发展显著促进了农民收入的持续增长。据此,本文提出应因地制宜地推进政策实施,创新发展农村产业融合的三种模式和加大科技人才支农力度等政策建议,以期为湖南省农村地区根据本地实际情况,有效推动农村产业融合,实现农民收入的增长提供有益的参考和启示。

数字化转型对制造企业竞争力的影响研究

在我国实体经济和数字经济融合发展的背景下,制造业的数字化转型升级已经成为提升企业市场竞争力的重要举措和我国建设制造强国的重要方向。基于此,本文利用2012—2022年制造业上市公司数据,运用固定效应模型实证检验了数字化转型对企业竞争力的影响以及技术创新在两者之间的作用机制,并进一步考察了供应链集中度的调节作用。结果表明:数字化转型对企业竞争力有显著的促进作用,此结果经过一系列稳健性检验后仍然成立。机制分析表明:数字化转型可以通过促进技术创新提高企业的竞争优势,供应链集中度负向调节数字化转型与企业竞争力之间的关系。异质性分析得出:数字化转型对企业竞争力的促进作用对于非国有企业和大中型企业更为明显。研究结论有利于厘清数字化转型与企业竞争力的关系,为制造业的转型升级提供理论依据。

数字经济发展对工业企业碳排放的影响机制研究

基于2013—2022年沪深A股工业企业数据,结合中国城市层面宏观指标,以“企业数字化转型-企业绿色创新”作为链条构建中介效应模型,研究数字经济发展对企业碳排放的作用机制。研究发现:数字经济发展显著抑制了工业企业碳排放;企业数字化转型及绿色技术创新在数字经济发展与企业碳排放之间发挥部分中介作用;存在“数字经济发展-企业数字化转型-企业绿色技术-企业碳排放”的链式中介作用。进一步的研究表明,数字经济的发展对碳减排的推动作用在非国有企业和西部地区企业中更加明显。政府应针对不同地区制定差异化的数字经济发展战略,企业应加强数字技术的研发与应用,通过构建数字化碳管理体系,推进工业企业低碳转型,助力“双碳”目标的实现。

供应链金融对企业运营绩效的影响研究:机理与实证

本文从企业专业化分工和全要素生产率两个视角,通过选取2014~2022中国资金密集型行业上市企业的样本数据,对供应链金融能否以及如何影响企业的运营绩效进行了机理分析与实证检验。结果表明:开展供应链金融业务能够显著提升企业自身的运营绩效,并且,促进企业的专业化分工和提升全要素生产率是供应链金融助力企业运营绩效提升的两个重要途径。本文不仅从供应链金融的视角为企业运营绩效的影响因素提供了新的解释,也揭示了供应链金融助力企业运营绩效提升的机制与途径。

Empirical Study on the Effect of Abolishing Agricultural Tax on Farmers' Income

The panel dataset which covered the socio-economic data of 31 provinces （municipalities and autonomous region） in China from 2000-2007 was used to do empirical analysis on the effect of abolishing agricultural taxes on farmers＇ income by referring to the fixed effect estimation method. It found that the abolition of agricultural taxes increased farmer＇s net income per capita by 2%. Combining with the results of empirical analysis, related policy suggestions were put forward to increase farmers＇ income.

Panel data models with cross-sectional dependence: a selective review

In this review, we highlight some recent methodological and theoretical develop- ments in estimation and testing of large panel data models with cross-sectional dependence. The paper begins with a discussion of issues of cross-sectional dependence, and introduces the concepts of weak and strong cross-sectional dependence. Then, the main attention is primarily paid to spatial and factor approaches for modeling cross-sectional dependence for both linear and nonlinear （nonparametric and semiparametric） panel data models. Finally, we conclude with some speculations on future research directions.

Bootstrap Inference on the Variance Component Functions in the Two-Way Random Effects Model with Interaction

In this paper,using the Bootstrap approach and generalized approach,the authors consider the one-sided hypothesis testing problems for variance component functions in the two-way random effects model.Firstly,the test statistics and confidence intervals for the sum of variance components are constructed.Next,the one-sided hypothesis testing problems for the ratio of variance components are also discussed.The Monte Carlo simulation results indicate that the Bootstrap approach is better than the generalized approach in most cases.Finally,the above approaches are applied to the real data examples of mice blood p H and molded plastic part’s dimensions.