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.

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.

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.

The Influence of China Demographic Structure on Social Security Expenditure—Based on Panel Data Model

Based on the panel data of 31 provinces and municipalities inChinafrom 1998 to 2016, this paper studies the effect of demographic structure on social security expenditure inChinaby using entity fixed effect regression model. The results show that there is a long-term co-integration relationship between population aging and social security expenditure in the demographic structure, and there is a positive correlation between population aging and social security expenditure. And the different cross-sectional effects in 31 regions of China reflect the difference between population aging and social security expenditure in different regions of China.

Nonparametric Estimation in Linear Mixed Models with Uncorrelated Homoscedastic Errors

Today, Linear Mixed Models (LMMs) are fitted, mostly, by assuming that random effects and errors have Gaussian distributions, therefore using Maximum Likelihood (ML) or REML estimation. However, for many data sets, that double assumption is unlikely to hold, particularly for the random effects, a crucial component in which assessment of magnitude is key in such modeling. Alternative fitting methods not relying on that assumption (as ANOVA ones and Rao’s MINQUE) apply, quite often, only to the very constrained class of variance components models. In this paper, a new computationally feasible estimation methodology is designed, first for the widely used class of 2-level (or longitudinal) LMMs with only assumption (beyond the usual basic ones) that residual errors are uncorrelated and homoscedastic, with no distributional assumption imposed on the random effects. A major asset of this new approach is that it yields nonnegative variance estimates and covariance matrices estimates which are symmetric and, at least, positive semi-definite. Furthermore, it is shown that when the LMM is, indeed, Gaussian, this new methodology differs from ML just through a slight variation in the denominator of the residual variance estimate. The new methodology actually generalizes to LMMs a well known nonparametric fitting procedure for standard Linear Models. Finally, the methodology is also extended to ANOVA LMMs, generalizing an old method by Henderson for ML estimation in such models under normality.

数字经济发展促进产业结构转型升级的实证研究

从理论上来说,数字经济发展通过影响产业间结构转变、产业内劳动生产率提升和产业资源配置效率提高等方式来促进产业结构转型升级。文章以我国247个地级及以上城市2016—2019年的数据为例,采用固定效应模型分析数字经济发展对产业结构转型升级的影响。实证结果发现:数字经济发展对第一维度、第二维度和第三维度产业结构转型升级均具有显著的促进作用,子样本数据的实证结果和采取替代变量的实证结果均支持这一研究结论;在控制内生性问题后,研究结论依然成立。进一步的机制检验发现,数字经济发展主要通过消费升级效应和技术外溢效应两种途径促进产业结构转型升级。

An Empirical Study on the Effectiveness of Technology Market Promoting Technological Innovation Capability—Based on panel data in the Yangtze River Delta

Based on panel data such as 2009-2018 R&D expenditures,full-time equivalent of R&D personnel,Number of valid invention patents,and technology market turnover in China's Shanghai,Jiangsu,Zhejiang,and Anhui provinces(cities),using Griliches-Jaffe knowledge to produce Function,construct Time fixed effects model,conduct empirical research on the main factors affecting the technological innovation capability of the Yangtze River Delta,focusing on the relationship between the technological market and technological innovation capability.The results show that human capital and R&D expenditures have a significant and positive impact on the technological innovation capacity of the Yangtze River Delta.Although the technology market has a positive effect on the scientific and technological innovation capacity of the Yangtze River Delta,the effect is not significant.Propose countermeasures and suggestions to strengthen the influence of the technology market on technological innovation.

数字普惠金融与农业绿色发展——基于金融约束缓解的视角

在农业生态化转型与可持续发展的背景下,农业绿色发展是实现农业高质量发展的现实路径,而作为新型金融模式的数字普惠金融,是打破农村金融困境的有效工具,已经成为农业绿色发展的重要驱动力。因此,论文利用中国2011-2020年30个省级面板数据,从金融约束缓解的视角,深入研究数字普惠金融对农业绿色发展的影响效应。研究发现:1.2011-2020年,农业绿色发展水平及其分指数,整体呈现上升趋势,但是,各区域间的农业绿色发展差异较大;2.数字普惠金融对农业绿色发展具有显著的促进作用,其中,数字普惠金融使用深度对农业绿色发展的影响程度最大;3.数字普惠金融能够通过缓解金融约束推动农业绿色发展;4.从农业重要性角度而言,数字普惠金融对粮食主产区农业绿色发展的影响程度最大,且其在粮食主产区和非粮食主产区中,均能通过缓解金融约束促进农业绿色发展。从区域差异性角度来看,数字普惠金融对农业绿色发展的影响程度,沿东部、中部向西部递减,且其均能通过缓解金融约束来促进东部和西部的农业绿色发展。为加快农业生产方式转型,推动农业绿色发展的进程,论文提出完善农村数字普惠金融市场、缩小区域“数字鸿沟”、增强数字普惠金融的农业绿色发展效能等政策建议。

数字经济与绿色物流的协同发展研究——以智慧城市试点城市为例

文章选取108个智慧城市试点城市2017-2021年面板数据,采用耦合协调模型,对试点城市中数字经济与绿色物流的协同程度进行评价。在此基础上,通过固定效应模型,检验试点城市中数字经济与绿色物流协同关系的影响因素。研究发现:108个智慧城市试点中,华北、华东地区中的试点城市耦合协调度相对较高,西北、东北地区中的试点城市耦合协调度相对较低,不同等级城市之间的耦合协调度存在梯度差异。经济发展水平、信息化水平以及技术行业人力资本对耦合协调关系存在显著相关关系,产业结构与环境建设对耦合协调发展均没有显著影响。

中国人口流动的健康轨迹研究

人口的高流动性和流动人口快速老化已成为中国人口发展的显著特征。研究人口流动对我国城乡居民健康的影响及其变动趋势,对于理解和实现健康老龄化,改善人口健康水平具有重要意义。现有研究较多分析流动人口相比于城镇居民的健康状况,而对流动人口和农村居民的健康差异的研究相对缺乏;对于流动人口在老年阶段的健康状况与户籍人口的差异以及流动对人口健康的影响等关键问题尚没有确定结论。本文旨在探讨人口流动与个体健康的因果关系以及流动人口和城乡户籍人口之间随时间和年龄变化的健康差异。本文利用中国家庭动态追踪调查(CFPS)2010-2020年连续六次调查的面板数据,采用固定效应模型和线性回归模型分析人口流动的健康效应和年龄效应。研究发现2012-2020年流动对人口健康的总体影响为负,验证了流动具有健康损耗效应,存在“流行病学悖论”。从时期维度看,流动对人口健康的影响呈现由健康优势转为健康劣势的趋势,人口流动的健康选择效应和流动经历的内在健康损耗效应并存。初期流动人口具有健康优势,健康自评状况优于城乡户籍人口。随着时间推进,健康优势逐渐收敛,健康损耗效应逐渐显现。从年龄维度看,流动人口和城乡户籍人口的自评健康水平随着年龄增加都呈下降趋势,但流动人口的下降速度显著慢于城乡户籍人口,其健康优势在老年阶段逐渐消失。上述发现的政策含义是:在人口老龄化不断加深和流动人口规模保持高位,流动人口老化愈加明显的背景下,流动人口的健康老龄化成为重要挑战,在政策实践层面应关注流动经历对个体生命周期的持续影响,尤其是要重视城镇中的高龄流动人口返乡回流将健康风险转移到农村,从而增加农村整体的健康脆弱性和健康照护成本,应将人口流动因素作为优化城乡卫生健康资源配置的重要考量。