治理的发轫与嬗变：中国历史视野下的考察

国内学术界一般以西方学术发展史作为背景，把治理作为一种社会互动来进行研究。中国历史视野下的治理，发轫于中国社会生活，其内涵最初主要是为了获取生存资料，即有收成才能够形成秩序也就是治理。从“乂”这个象形文字来看，乂就是治理，其原意为“收割”。不过，围绕生存资料的生产和分配逐渐产生了更加丰富的社会权力关系，而巩固和拓展这种权力关系则使得构筑更加严格的秩序成为必然。神秘感是构筑秩序的重要依据，而赋予仪式以神秘感的则是觋、巫等知识群体，他们将天、帝、王、人通过仪式联系在一起，建构起一套以礼为基础的秩序。但是，西周末年，觋、巫的神秘感被世俗的君权摧毁以后，“君臣秩序”以及由此构筑起来的社会秩序都被颠覆，从而导致“君臣之礼既坏”和“礼坏乐崩”的情形。“以智力相雄长”不仅表现在王室内部、诸侯之间、社会层面上，而且也表现在知识分子身上。从此以后，士绝大多数以追逐爵禄而骄天下为荣，以至于两千多年来一直存在“士贱而君肆”的情形。“君士互动”的治理模式，正是随着神秘主义的礼的崩溃而崩溃的。     

基于MS-AR模型的中国能源消费周期路径演化识别

文章基于改革开放以来的煤炭、石油、天然气、一次电力及其他能源消费增长率年度数据,运用HP滤波和MS-AR模型,透析能源消费周期多阶段的区制属性,刻画我国能源消费增速的时间路径变化特征。结果表明:(1)除一次电力及其他能源消费增长率外,各能源消费增长率均处于窄幅震荡状态,21世纪初呈现明显的下行态势;煤炭、石油增长率波动程度明显强于天然气、一次电力及其他能源消费增长率波动程度。(2)各能源消费增长率存在低速增长区制、中速增长区制、高速增长区制的三区制特征;以煤炭、石油为代表的传统能源消费增长率在低速和高速增长区制间动态跃迁,仅在相关政策、重大改革方针出台后跃入高速增长区制,在中速增长区制具有持续性;天然气消费增长率在低速增长区制具有惰性;21世纪以来,一次电力及其他能源消费增长率维持在高增长区制。(3)区制特征与波动风险具有联动效应,高(低)增长区制具有高(低)波动风险。     

局部平稳部分线性面板数据模型的统计推断

部分线性模型是一类非常重要的半参数回归模型,由于它既含有参数部分又含有非参数部分,与常规的线性模型相比具有更强的适应性和解释能力。文章研究带有局部平稳协变量的固定效应部分线性面板数据模型的统计推断。首先提出一个两阶段估计方法得到模型中未知参数和非参数函数的估计,并证明估计量的渐近性质,然后运用不变原理构造出非参数函数的一致置信带,最后通过数值模拟研究和实例分析验证了该方法的有效性。     

工业劳动力流动、资本创造与经济地理空间均衡

资本创造模型（CC模型）忽视了要素流动对产业空间分布的影响。而发展的新的资本创造模型则认为资本集聚的过程必然伴随着工业劳动力的流动过程。另外，是资本的实际收益而不是名义收益决定资本是否创造。研究结果表明，随着贸易自由度、工业品支出份额及资本贴现率的变大，替代弹性及资本折旧率的变小，将降低对称结构的稳定性，而提高中心-外围结构的稳定性；经济地理空间的产业均衡是集聚力和分散力相互作用的结果。当企业生产工业品的规模报酬递增程度足够显著，或者工业品支出份额很高时，市场拥挤效应将彻底消失，并转化成为促进产业集聚的动力；突破点与持续点的大小比较可以形成不同的关系，这意味着随着贸易自由度的变化，本文发展的资本创造模型可以体现出多样化的产业空间动态演化行为。     

Midwives’ views of caseload midwifery – comparing the caseload and non-caseload midwives’ opinions. A cross-sectional survey of Australian midwives.

BackgroundMidwife-led continuity of care has substantial benefits for women and infants and positive outcomes for midwives, yet access to these models remains limited. Caseload midwifery is associated with professional satisfaction and lower burnout, but also impacts on work-life boundaries. Few studies have explored caseload midwifery from the perspective of midwives working in caseload models compared to those in standard care models, understanding this is critical to sustainability and upscaling.AimTo compare views of caseload midwifery – those working in caseload models and those in standard care models in hospitals with and without caseload.MethodsA national cross-sectional survey of midwives working in Australian public hospitals providing birthing services.FindingsResponses were received from 542/3850 (14%) midwives from 111 hospitals – 20% worked in caseload, 39% worked in hospitals with caseload but did not work in the model, and 41% worked in hospitals without caseload. Regardless of exposure, midwives expressed support for caseload models, and for increased access to all women regardless of risk. Fifty percent of midwives not working in caseload expressed willingness to work in the model in the future. Flexibility, autonomy and building relationships were positive influencing factors, with on-call work the most common reason midwives did not want to work in caseload.ConclusionsThere was widespread support for and willingness to work in caseload. The findings suggest that the workforce could support increasing access to caseload models at existing and new caseload sites. Exposure to the model provides insight into understanding how the model works, which can positively or negatively influence midwives’ views.     

Approximate composite marginal likelihood inference in spatial generalized linear mixed models

Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects. The likelihood function of this model cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. There are numerical ways to maximize the likelihood function, such as Monte Carlo Expectation Maximization and Quadrature Pairwise Expectation Maximization algorithms. They can be applied but may in such cases be computationally very slow or even prohibitive. Gauss–Hermite quadrature approximation only suitable for low-dimensional latent variables and its accuracy depends on the number of quadrature points. Here, we propose a new approximate pairwise maximum likelihood method to the inference of the spatial generalized linear mixed model. This approximate method is fast and deterministic, using no sampling-based strategies. The performance of the proposed method is illustrated through two simulation examples and practical aspects are investigated through a case study on a rainfall data set.     

Noninformative priors for the between-group variance in the unbalanced one-way random effects model with heterogeneous error variances

For the unbalanced one-way random effects model with heterogeneous error variances, we propose the non-informative priors for the between-group variance and develop the first- and second-order matching priors. It turns out that the second-order matching priors do not exist and the reference prior and Jeffreys prior do not satisfy a first-order matching criterion. We also show that the first-order matching prior meets the frequentist target coverage probabilities much better than the Jeffreys prior and reference prior through simulation study, and the Bayesian credible intervals based on the matching prior and reference prior give shorter intervals than the existing confidence intervals by examples.     

Improved two-parameter estimators for the negative binomial and Poisson regression models

Negative binomial regression (NBR) and Poisson regression (PR) applications have become very popular in the analysis of count data in recent years. However, if there is a high degree of relationship between the independent variables, the problem of multicollinearity arises in these models. We introduce new two-parameter estimators (TPEs) for the NBR and the PR models by unifying the two-parameter estimator (TPE) of Özkale and Kaç?ranlar [The restricted and unrestricted two-parameter estimators. Commun Stat Theory Methods. 2007;36:2707–2725]. These new estimators are general estimators which include maximum likelihood (ML) estimator, ridge estimator (RE), Liu estimator (LE) and contraction estimator (CE) as special cases. Furthermore, biasing parameters of these estimators are given and a Monte Carlo simulation is done to evaluate the performance of these estimators using mean square error (MSE) criterion. The benefits of the new TPEs are also illustrated in an empirical application. The results show that the new proposed TPEs for the NBR and the PR models are better than the ML estimator, the RE and the LE.     

The effect of small sample on optimal designs for logistic regression models

Asymptotic methods are commonly used in statistical inference for unknown parameters in binary data models. These methods are based on large sample theory, a condition which may be in conflict with small sample size and hence leads to poor results in the optimal designs theory. In this paper, we apply the second order expansions of the maximum likelihood estimator and derive a matrix formula for the mean square error (MSE) to obtain more precise optimal designs based on the MSE. Numerical results indicate the new optimal designs are more efficient than the optimal designs based on the information matrix.