随机效应广义空间滞后半参数变系数面板模型的估计

通过构建同时包含因变量和误差项空间滞后的随机效应半参数变系数面板模型,拓展了现有模型的灵活性和适应性。采用截面极大似然估计方法得出了参数和非参数的估计,理论证明发现：在一定的正则条件下,所有估计量具有一致性和渐近正态性。数值模拟显示：估计量具有良好的小样本性质,估计精度随着样本容量的增加而增加;空间权重矩阵的选择对估计量的表现没有产生显著差异,但是在Case权重矩阵下,当样本量相同时,空间相关系数的估计偏差随着空间权重结构复杂度的增加而扩大。     

空间滞后误差自相关随机前沿模型参数的估计

文章利用工具变量矩阵方法研究了空间滞后误差自相关随机前沿模型参数的估计问题,得到了参数估计的表达式.与极大似然估计相比较,工具变量矩阵法极大地简化了计算.蒙特卡罗模拟的结果表明,参数估计值十分逼近真值.     

证券市场中板块联动效应的空间计量分析

收益率之间的联动效应广泛存在于证券市场中,文章用空间计量的方法对同板块股票收益率的联动效应进行了定量分析,在CAPM模型的基础上,通过使用空间权重矩阵将联动影响因素纳入了解释变量之中,并推导出极大似然估计方法克服了内生性问题,然后通过蒙特卡洛模拟证明了这种方法的可行性。     

空间滞后分位数回归模型的工具变量估计及参数检验

本文提出一种遵循空间数据分布特征的空间分位数回归模型,并着重探讨该模型的估计方法和参数检验问题。本文构建了上述模型的一个工具变量估计法,通过数理证明建立了估计量的大样本理论,并基于估计量的渐近分布构造了模型的参数检验方法。本文还通过数值模拟方法和应用实例考察估计方法和参数检验方法的实际应用效果,数值模拟结果显示,估计方法和参数检验方法在有限样本条件下均可以达到较高的精确度和稳定性。在应用实例中,本文利用所构建的理论方法重新检验我国“资源诅咒”效应的存在性,实证结果体现了理论方法的应用价值。     

一种新的空间权重矩阵构造及其应用

空间权重矩阵的构造一直是地理学、空间计量经济学和医学等领域中的难点和热点问题.权重矩阵合适与否,将直接关系到模型的最终估计结果.文章基于渗透搜索算法,通过局部MoranI的比较,得到新的“邻接”关系区域,并结合基于经济距离的权重构造出新的权重矩阵.     

广义嵌套空间模型变量选择研究——基于广义空间信息准则

本文提出了广义空间信息准则,以解决广义嵌套空间模型的变量选择问题.依据大样本性质的不同,将该准则分为两类:空间AIC类准则和空间BIC类准则.研究发现,空间AIC类准则能有效解决空间模型中变量的错选和漏选问题,但存在多选变量的倾向;而空间BIC类准则能同时解决空间模型中变量的错选、漏选和多选问题,而且在特殊条件下能更有效解决错选和漏选问题,但往往需要更大的样本容量.Monte Carlo模拟结果印证了上述相关结论.最后,本文以城市对外资银行的吸引力为例,在给定测度指标的基础上,验证其空间相关性,并利用本文提出的方法对其影响因素进行变量选择.     

基于空间模型的小域估计方法

小域估计成为当今抽样调查的热点问题之一，日益受到社会各界的关注。小域估计多采用基于模型的估计方法，其中以线性混合模型最为普遍，这种模型通常假定域随机效应是独立的。但是，在实际各个域之间往往表现出一定的空间相关性，并且这种相关性随着距离的增加而减小，若忽视这种空间效应，估计的精度会大大的降低。本文运用域随机效应为空间相关的空间模型来解决空间数据下的小域估计问题，并用基于这种空间模型的权数的方法得到了目标变量的稳健估计量，很大程度上提高小域估计的精度，是一种比较好的小域估计方法。     

基于随机化适应性Lasso的高维变量选择

Lasso等惩罚变量选择方法选入模型的变量数受到样本量限制。文献中已有研究变量系数显著性的方法舍弃了未选入模型的变量含有的信息。本文在变量数大于样本量即p>n的高维情况下,使用随机化bootstrap方法获得变量权重,在计算适应性Lasso时构建选择事件的条件分布并剔除系数不显著的变量,以得到最终估计结果。本文的创新点在于提出的方法突破了适应性Lasso可选变量数的限制,当观测数据含有大量干扰变量时能够有效地识别出真实变量与干扰变量。与现有的惩罚变量选择方法相比,多种情境下的模拟研究展示了所提方法在上述两个问题中的优越性。实证研究中对NCI-60癌症细胞系数据进行了分析,结果较以往文献有明显改善。     

外商直接投资与中国雾霾污染

本文利用1998-2012年中国30个省份的空间面板数据,采用Moran‘s I和Gear's C指数对FDI与雾霾(PM2.5)污染进行了探索性空间数据分析,并基于EKC假说构建了空间面板数据模型,将经济地理嵌套权重矩阵、经济地理权重矩阵纳入静态和动态空间面板模型进行分析.实证结果发现:①相对于静态空间面板模型,动态空间计量模型能更为准确地拟合FDI对我国雾霾(PM2.5)浓度的影响过程,雾霾(PM2.5)污染表现出显著的“叠加效应”和“溢出效应”.②在经济地理嵌套权重矩阵和经济地理权重矩阵下,FDI流量每升高1％,PM25浓度分别升高0.0174％和0.0161％.FDI存量每升高1％,PM2.5浓度分别升高0.0177％和0.0163％,表明FDI是导致雾霾(PM25)浓度升高的影响因素之一,说明了我国目前吸引和利用FDI离环保目标的最优水平还有一定距离.     

空间计量模型中空间矩阵的误用及其影响

 本文通过研究指出,基于常用的空间计量经济学模型嵌套检验方法,我们经常会遇到空间结构关系误用的情况。通过设计一个Monte Carlo实验,本文讨论了空间矩阵误用的概率、影响空间矩阵误用的因素以及空间矩阵误用对空间计量经济学模型估计造成的影响等三个问题。     

Inference for $$L_2$$L2 -Boosting

We propose a statistical inference framework for the component-wise functional gradient descent algorithm (CFGD) under normality assumption for model errors, also known as $$L_2$$-Boosting. The CFGD is one of the most versatile tools to analyze data, because it scales well to high-dimensional data sets, allows for a very flexible definition of additive regression models and incorporates inbuilt variable selection. Due to the variable selection, we build on recent proposals for post-selection inference. However, the iterative nature of component-wise boosting, which can repeatedly select the same component to update, necessitates adaptations and extensions to existing approaches. We propose tests and confidence intervals for linear, grouped and penalized additive model components selected by $$L_2$$-Boosting. Our concepts also transfer to slow-learning algorithms more generally, and to other selection techniques which restrict the response space to more complex sets than polyhedra. We apply our framework to an additive model for sales prices of residential apartments and investigate the properties of our concepts in simulation studies.     

Performance of information criteria for spatial models

Model choice is one of the most crucial aspect in any statistical data analysis. It is well known that most models are just an approximation to the true data-generating process but among such model approximations, it is our goal to select the ‘best’ one. Researchers typically consider a finite number of plausible models in statistical applications, and the related statistical inference depends on the chosen model. Hence, model comparison is required to identify the ‘best’ model among several such candidate models. This article considers the problem of model selection for spatial data. The issue of model selection for spatial models has been addressed in the literature by the use of traditional information criteria-based methods, even though such criteria have been developed based on the assumption of independent observations. We evaluate the performance of some of the popular model selection critera via Monte Carlo simulation experiments using small to moderate samples. In particular, we compare the performance of some of the most popular information criteria such as Akaike information criterion (AIC), Bayesian information criterion, and corrected AIC in selecting the true model. The ability of these criteria to select the correct model is evaluated under several scenarios. This comparison is made using various spatial covariance models ranging from stationary isotropic to nonstationary models.     

A consistent model selection for orthogonal regression under component-wise shrinkage

Several authors developed a series of model selection criteria for determining the major frequency components in harmonic analysis. In this paper, we considered another direction of the extension. Specifically, we proposed a model selection criterion for an orthogonal regression estimated with a component-wise shrinkage method and proved the consistency of the proposed criterion. Through simple numerical examples, we verified the performance of the proposed criterion with the empirical component-wise shrinkage estimator. Our criterion is fully empirical and thus can be applied directly for practical uses.     

空间计量模型选择及其模拟分析

空间计量模型的选择是空间计量建模的一个重要组成部分,也是空间计量模型实证分析的关键步骤。本文对空间计量模型选择中的Moran指数检验、LM检验、似然函数、三大信息准则、贝叶斯后验概率、马尔可夫链蒙特卡罗方法做了详细的理论分析。并在此基础之上,通过Matlab编程进行模拟分析,结果表明：在扩充的空间计量模型族中进行模型选择时,基于OLS残差的Moran指数与LM检验均存在较大的局限性,对数似然值最大原则缺少区分度,LM检验只针对SEM和SAR模型的区分有效,信息准则对大多数模型有效,但是也会出现误选。而当给出恰当的M-H算法时,充分利用了似然函数和先验信息的MCMC方法,具有更高的检验效度,特别是在较大的样本条件下得到了完全准确的判断,且对不同阶空间邻接矩阵的空间计量模型的选择也非常有效。     

Detection and Estimation of Block Structure in Spatial Weight Matrix

In many economic applications, it is often of interest to categorize, classify, or label individuals by groups based on similarity of observed behavior. We propose a method that captures group affiliation or, equivalently, estimates the block structure of a neighboring matrix embedded in a Spatial Econometric model. The main results of the Least Absolute Shrinkage and Selection Operator (Lasso) estimator shows that off-diagonal block elements are estimated as zeros with high probability, property defined as “zero-block consistency.” Furthermore, we present and prove zero-block consistency for the estimated spatial weight matrix even under a thin margin of interaction between groups. The tool developed in this article can be used as a verification of block structure by applied researchers, or as an exploration tool for estimating unknown block structures. We analyzed the U.S. Senate voting data and correctly identified blocks based on party affiliations. Simulations also show that the method performs well.     

General Sparse Boosting: Improving Feature Selection of L2 Boosting by Correlation-Based Penalty Family

In high-dimensional setting, componentwise L₂boosting has been used to construct sparse model that performs well, but it tends to select many ineffective variables. Several sparse boosting methods, such as, SparseL₂Boosting and Twin Boosting, have been proposed to improve the variable selection of L₂boosting algorithm. In this article, we propose a new general sparse boosting method (GSBoosting). The relations are established between GSBoosting and other well known regularized variable selection methods in the orthogonal linear model, such as adaptive Lasso, hard thresholds, etc. Simulation results show that GSBoosting has good performance in both prediction and variable selection.     

空间经济计量滞后模型Bootstrap Moran检验功效的模拟分析

 当误差项不服从独立同分布时,利用Moran’s I统计量的渐近检验,无法有效判断空间经济计量滞后模型2SLS估计残差间存在空间关系与否。本文采用两种基于残差的Bootstrap方法,诊断空间经济计量滞后模型残差中的空间相关关系。大量Monte Carlo模拟结果显示,从功效角度看,无论误差项服从独立同分布与否,与渐近检验相比,Bootstrap Moran检验都具有更好的有限样本性质,能够更有效地进行空间相关性检验。尤其是,在样本量较小和空间衔接密度较高情况下,Bootstrap Moran检验的功效显著大于渐近检验。     

空间外溢视角下的协同创新与区域产业升级

本文从空间维度廓清了协同创新对区域产业升级的空间外溢机制和外溢效应边界形成机理。采用中国分省区面板数据,利用地理距离和社会经济特征多种空间权重矩阵设定的空间杜宾(Durbin)模型实证检验了协同创新对产业升级的影响。结果表明：协同创新对产业升级既有区域内溢出效应,也有区域间溢出效应。空间外溢效应在很大程度上通过创新要素区际流动来实现,且呈现出地理距离衰减特征。进一步考虑地理区位特征差异发现：空间外溢对产业升级的带动效应在中部地区最强,西部地区次之,东部地区最弱。本研究结果有助于全面认识协同创新作用机制,对推动区域间良性互动,实现产业升级发展具有重要现实意义。     

Variable Selection in Joint Location and Scale Models of the Skew-t-Normal Distribution

Variable selection is an important issue in all regression analysis, and in this article, we investigate the simultaneous variable selection in joint location and scale models of the skew-t-normal distribution when the dataset under consideration involves heavy tail and asymmetric outcomes. We propose a unified penalized likelihood method which can simultaneously select significant variables in the location and scale models. Furthermore, the proposed variable selection method can simultaneously perform parameter estimation and variable selection in the location and scale models. With appropriate selection of the tuning parameters, we establish the consistency and the oracle property of the regularized estimators. These estimators are compared by simulation studies.