客户空间行为的空间交互和离散选择模型研究

客户空间行为模型可以帮助解释空间竞争态势、提供管理建议、预测销售以及选址决策.在以往相关研究的基础上,深入研究空间结构对客户空间行为的影响,指出相对空间位置的重要作用,提出相对空间位置变量及度量方法.在离散选择模型的框架下,结合相对空间位置变量的客户空间行为模型更好地解释了空间结构对空间行为的影响,提高了参数空间无关性.在一个真实的城市空间中调查客户超市选择行为数据,并基于这些数据验证了模型有效性.     

Inference under pivotal sampling: Properties,variance estimation,and application to tesselation for spatial sampling

Unequal probability sampling is commonly used for sample selection. In the context of spatial sampling, the variables of interest often present a positive spatial correlation, so that it is intuitively relevant to select spatially balanced samples. In this article, we study the properties of pivotal sampling and propose an application to tesselation for spatial sampling. We also propose a simple conservative variance estimator. We show that the proposed sampling design is spatially well balanced, with good statistical properties and is computationally very efficient.     

How strong is the Social Security safety net? Using the Elder Index to assess gaps in economic security

Older Americans rely heavily on Social Security benefits (SSBs) to support independent lifestyles, and many have few or no additional sources of income. We establish the extent to which SSBs adequately support economic security, benchmarked by the Elder Economic Security Standard Index. We document variability across U.S. counties in the adequacy levels of SSBs among older adults. We find that the average SSBs fall short of what is required for economic security in every county in the United States, but the level of shortfall varies considerably by location. Policy implications relating to strengthening Social Security and other forms of retirement income are discussed.     

A study on geographically weighted spatial autoregression models with spatial autoregressive disturbances

Abstract

Spatial heterogeneity and correlation are both considered in the geographical weighted spatial autoregressive model. At present, this kind of model has aroused the attention of some scholars. For the estimation of the model, the existing research is based on the assumption that the error terms are independent and identically distributed. In this article we use a computationally simple procedure for estimating the model with spatially autoregressive disturbance terms, both the estimates of constant coefficients and variable coefficients are obtained. Finally, we give the large sample properties of the estimators under some ordinary conditions. In addition, application study of the estimation methods involved will be further explored in a separate study.     

Local linear estimate of the point at high risk: Spatial functional data case

Abstract

The purpose of the present paper is to investigate by the local linear method a nonparametric estimator of the point at high risk of scalar response variable given a functional variable when the observations are spatially dependent. The main goal is to establish the almost complete convergence with rate of this estimator under some general conditions. A practical example on the climatological data shows the usefulness of our theoretical study.     

区域旅游竞争力评价与空间格局演化研究——以广东省21个地市为例

文章针对现有相关研究存在的不足，重新构建区域旅游竞争力评价指标体系，利用熵值法定量评价2010—2016年广东21个地市旅游竞争力，运用空间聚类分析法分析其差异性特征，利用全局和局部空间自相关分析法探讨其空间演化特征。结果表明：广东省内区域旅游竞争力水平差异明显，主要表现为珠三角城市的旅游竞争力水平普遍较高，其他地市大多较低；各地市旅游竞争力整体上有明显的自相关性，呈现较强的集聚效应，但该效应有下降趋势；各地市旅游竞争力已形成较稳定的空间格局，即中间突出、四周凹陷；各地市旅游竞争力水平在空间上的局部自相关性不显著；热—冷点区域的空间分布呈现出由珠三角城市逐渐向四周推移的圈级特征，形成了以珠三角城市群为热点区的核心—边缘结构。基于研究结论，文章提出需在继续保持珠三角地区旅游竞争力优势的同时增强其辐射和带动作用，加大政府对粤东、粤西、粤北地区旅游业发展的支持力度，加强区域（城市）间的交流与合作，推动区域旅游一体化建设等建议。文章可为其他区域旅游竞争力评价及空间格局演化研究提供有益借鉴，为促进区域旅游和经济发展提供决策参考。     

GMM estimation of spatial autoregressive models in a system of simultaneous equations with heteroskedasticity

This paper proposes a GMM estimation framework for the SAR model in a system of simultaneous equations with heteroskedastic disturbances. Besides linear moment conditions, the proposed GMM estimator also utilizes quadratic moment conditions based on the covariance structure of model disturbances within and across equations. Compared with the QML approach, the GMM estimator is easier to implement and robust under heteroskedasticity of unknown form. We derive the heteroskedasticity-robust standard error for the GMM estimator. Monte Carlo experiments show that the proposed GMM estimator performs well in finite samples.     

国外幼儿空间语言发展的影响因素研究述评

空间语言作为幼儿语言交流的组成部分，对幼儿空间技能的发展有促进作用。通过对近十年国外关于空间语言的文献进行梳理，可以看出影响幼儿空间语言的因素有游戏环境、成人的空间语言和所提供的“脚手架”、幼儿的年龄、性别，以及手势语言等。成人可通过为幼儿空间语言的产生创造一个适宜的游戏环境、多用空间语言与幼儿进行交流、重视手势语言的作用等几方面来促进幼儿空间语言的发展。     

Parameter estimation and inference with spatial lags and cointegration

This article studies dynamic panel data models in which the long run outcome for a particular cross-section is affected by a weighted average of the outcomes in the other cross-sections. We show that imposing such a structure implies a model with several cointegrating relationships that, unlike in the standard case, are nonlinear in the coe?cients to be estimated. Assuming that the weights are exogenously given, we extend the dynamic ordinary least squares methodology and provide a dynamic two-stage least squares estimator. We derive the large sample properties of our proposed estimator under a set of low-level assumptions. Then our methodology is applied to US financial market data, which consist of credit default swap spreads, as well as firm-specific and industry data. We construct the economic space using a “closeness” measure for firms based on input–output matrices. Our estimates show that this particular form of spatial correlation of credit default swap spreads is substantial and highly significant.     

Threshold knot selection for large-scale spatial models with applications to the Deepwater Horizon disaster

Large spatial datasets are typically modelled through a small set of knot locations; often these locations are specified by the investigator by arbitrary criteria. Existing methods of estimating the locations of knots assume their number is known a priori, or are otherwise computationally intensive. We develop a computationally efficient method of estimating both the location and number of knots for spatial mixed effects models. Our proposed algorithm, Threshold Knot Selection (TKS), estimates knot locations by identifying clusters of large residuals and placing a knot in the centroid of those clusters. We conduct a simulation study showing TKS in relation to several comparable methods of estimating knot locations. Our case study utilizes data of particulate matter concentrations collected during the course of the response and clean-up effort from the 2010 Deepwater Horizon oil spill in the Gulf of Mexico.