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。     

On Bartlett correction of empirical likelihood for regularly spaced spatial data

Bartlett correction constitutes one of the attractive features of empirical likelihood because it enables the construction of confidence regions for parameters with improved coverage probabilities. We study the Bartlett correction of spatial frequency domain empirical likelihood (SFDEL) based on general spectral estimating functions for regularly spaced spatial data. This general formulation can be applied to testing and estimation problems in spatial analysis, for example testing covariance isotropy, testing covariance separability as well as estimating the parameters of spatial covariance models. We show that the SFDEL is Bartlett correctable. In particular, the improvement in coverage accuracies of the Bartlett‐corrected confidence regions depends on the underlying spatial structures. The Canadian Journal of Statistics 47: 455–472; 2019 © 2019 Statistical Society of Canada     

区域一体化背景下城市土地利用效率的空间关联及其解释——以长江中游城市群为例

从理论层面揭示区域一体化影响城市土地利用效率（ULUE）的内在机理，构建区域一体化背景下“规模+结构+集聚”的ULUE测度体系，并综合利用小波神经网络、空间分异模型和空间回归分析探索2003-2015年长江中游城市群ULUE的空间关联特征及其影响因素。结果发现：①区域一体化背景下长江中游城市群ULUE从2003的0.561 4增长至2015年的0.811 1，且呈现出明显的区域差异。②ULUE并非随机分布，而是具有空间上的关联特征和集群趋势。且由于不同集聚区内扩散效应在空间上渗透不均衡，其关联性主要表现为空间依赖性和空间异质性。③区域一体化背景下长江中游城市群ULUE在空间关联上具有“近水楼台先得月”和“门当户对”的特征，地理位置的空间相邻、投资消费结构和产业结构的相似是其空间关联的主要成因。     

安徽省大别山区旅游扶贫效率时空格局演化及影响机理

采用DEA模型对安徽省大别山区旅游扶贫效率的综合测度影响因素分析可知:安徽省大别山区旅游扶贫效率处于中等偏低水平,呈现波动上升的趋势,主要受规模效率驱动;旅游扶贫规模效率和旅游扶贫综合效率的空间分异特征较为类似,均呈现出区域中间高南北低的分布,旅游扶贫纯技术效率高值区呈现出由区域北部和南部片状集中分布逐渐向四周不断分散分布的演化态势;投入规模、人力支持水平、可达性水平、信息化水平对大别山区旅游扶贫效率产生正向影响,且影响强弱呈现为信息化水平>人力支持>可达性水平>投入规模。     

半参数空间ZISF的估计、分类与蒙特卡罗模拟

零无效率随机前沿模型（ZISF）包含随机前沿模型和回归模型，两模型各有一定的发生概率，适用于技术无效生产单元和技术有效生产单元同时存在的情形。本文在ZISF的生产函数中引入空间效应和非参函数，并假设回归模型的发生概率为非参函数，构建了半参数空间ZISF。该模型可有效避免忽略空间效应导致的有偏且不一致估计量，也避免了线性模型的拟合不足。本文对非参函数采用B样条逼近，使用极大似然方法和JLMS法分别估计参数和技术效率。蒙特卡罗结果表明：①本文方法的估计精度和分类精度均较高。随着样本容量的增大，精度增加。②忽略空间效应或者非参数效应，估计精度和分类精度降低，文中模型有存在必要性。③忽略发生概率的非参数效应会严重降低估计和分类精度，远大于忽略生产函数的非参数效应的影响。     

二语情感抽象概念加工的具身认知研究

基于具身认知框架下的概念隐喻理论，对中国英语学习者抽象情感词加工的具身认知进行了实证研究。结果发现:（1）二语情感抽象概念存在“积极在上，消极在下”的垂直空间隐喻映射；（2）不同水平组对二语抽象情感词的加工成本存在不对称性；（3）中国英语学习者对不同效价的抽象情感词加工成本趋异。实验结果证明了情感抽象概念能够通过隐喻的方式建立与感知运动信息的联系，情感模拟对二语抽象情感词的加工具有促进作用。     

陕西省交通基础设施对经济增长的空间溢出效应

为了从外部性角度说明目前陕西省交通基础设施的建设和投资在地域上存在的问题，为陕西省交通基础设施建设方向提供理论帮助，基于陕西省2009~2017年间各市相关数据，在交通基础设施对经济增长效应研究的直接效应和溢出效应基础上，加入空间要素，构建生产函数和空间权重矩阵，通过自相关检验（莫兰指数）、LM检验和 Hausman检验，选取合适的空间面板计量模型对陕西省交通基础设施的经济溢出情况进行研究。研究认为，2009~2017年间陕西省交通基础设施对经济增长的溢出效应（显著）整体为负，但依然存在正溢出的局部地区，即以西安为首的经济聚集区（西安、宝鸡、咸阳）和榆林、延安联合的陕北经济聚集区，资源过于集中和两个高地之间缺乏互联互通是整体呈现负溢出的重要原因。在地域分配的量和质上应充分考虑交通基础设施的溢出效应，避免资源过度集中在西安，在两个经济高地之间打通一条经济要道，并重点扶持相对落后又有发展前景的城市可促使陕西经济发展由总体集聚向总体扩散转变。     

德语空间性状形容词的时间映射特质研究

基于认知隐喻理论，选择德语中14个表征空间性状的形容词，探究其时间映射特质。研究发现:长/短、大/小、远/近、高、深可表征时间，其余则无法表征时间。具体特质如下:存在“一维＞二维＞三维”映射维度等级；存在“长短＞远近＞高深/大小”映射宽幅等级；正向形容词比负向更易表征时间。这种映射特质主要与时空相似性、认知思维及隐喻操作有关。     

Variable selection and estimation for high-dimensional spatial autoregressive models

Spatial regression models are important tools for many scientific disciplines including economics, business, and social science. In this article, we investigate postmodel selection estimators that apply least squares estimation to the model selected by penalized estimation in high-dimensional regression models with spatial autoregressive errors. We show that by separating the model selection and estimation process, the postmodel selection estimator performs at least as well as the simultaneous variable selection and estimation method in terms of the rate of convergence. Moreover, under perfect model selection, the ℓ₂ rate of convergence is the oracle rate of $\sqrt{s/n}$, compared with the convergence rate of $\text{◂√▸}\sqrt{s\log p/n}$ in the general case. Here, n is the sample size and p, s are the model dimension and number of significant covariates, respectively. We further provide the convergence rate of the estimation error in the form of $\sup$ norm, and ideally the rate can reach as fast as $\text{◂√▸}\sqrt{\log s/n}$.     

Spatial Risk Analysis of Power Systems Resilience During Extreme Events

The increased frequency of extreme events in recent years highlights the emerging need for the development of methods that could contribute to the mitigation of the impact of such events on critical infrastructures, as well as boost their resilience against them. This article proposes an online spatial risk analysis capable of providing an indication of the evolving risk of power systems regions subject to extreme events. A Severity Risk Index (SRI) with the support of real‐time monitoring assesses the impact of the extreme events on the power system resilience, with application to the effect of windstorms on transmission networks. The index considers the spatial and temporal evolution of the extreme event, system operating conditions, and the degraded system performance during the event. SRI is based on probabilistic risk by condensing the probability and impact of possible failure scenarios while the event is spatially moving across a power system. Due to the large number of possible failures during an extreme event, a scenario generation and reduction algorithm is applied in order to reduce the computation time. SRI provides the operator with a probabilistic assessment that could lead to effective resilience‐based decisions for risk mitigation. The IEEE 24‐bus Reliability Test System has been used to demonstrate the effectiveness of the proposed online risk analysis, which was embedded in a sequential Monte Carlo simulation for capturing the spatiotemporal effects of extreme events and evaluating the effectiveness of the proposed method.