"一带一路"与人文发展的空间依赖性分析

基于2014年"一带一路"沿线61个国家的人文发展指数,利用Moran'I空间自相关统计量检验"一带一路"沿线国家人文发展指数之间的空间自相关性,利用空间误差模型研究了"一带一路"战略对沿线国家的教育、医疗卫生等指数的影响.结果表明:"一带一路"沿线61国人文发展水平之间存在明显的空间自相关性,即空间聚集性和异质性并存;"一带一路"战略的实施必将对沿线国的教育、医疗卫生等发展做出积极贡献.     

我国纺织业集聚分布格局及其影响因素的空间面板数据分析

在我国,关于区域产业集聚的理论研究进展迅速,但基于空间计量经济模型的实证研究仍需完善。本文采用中国2004-2007省际面板数据,验证纺织业集聚的空间依赖性存在,并分别对空间滞后模型和空间误差模型进行估计,得出以下结论:近些年纺织业集聚的省际分布格局主要呈现"东高西低"的两极化特征,总体变化不大;根据全局Moran I指数和LISA集聚图,纺织业集聚存在空间维度的依赖性和异质性,东部沿海纺织业的扩散作用没有充分发挥,区域发展很不平衡;原料供给量、平均规模水平、交通运输条件等因素都对纺织业集聚产生不同程度的正向影响,而地方保护阻碍产业跨区迁移与形成集聚;与理论假设不同,低廉的劳动力成本对纺织业集聚没有产生明显的促进作用,主要是受到基础设施状况和运输条件的限制;制定相关政策的同时,注意从以上这些要素入手,还必须充分考虑空间作用机制对纺织业省域集聚的差异化作用。     

中国八大综合经济区区域性金融风险分布动态、空间差异及收敛性研究

精准施策治理区域性金融风险的前提是掌握区域性金融风险的地区异质性。本文研究了中国八大综合经济区2000年～2018年区域性金融风险的分布动态、空间差异与空间收敛性。研究发现：作为区域性金融风险总和的系统性金融风险水平从多峰低值向单峰高值演进且呈现明显的周期性变动规律,当前我国的系统性金融风险正处于新的爬坡期;考察期内八大综合经济区区域性金融风险差异空间异质性特征突出,区域间差异是总体差异的主要来源;不同区域金融风险拥有各自稳态水平且影响因素各不相同,在管控区域性金融风险时应因地制宜,精准施策。研究结论对于从区域层面入手控制区域性金融风险水平,守住不发生系统性金融风险的底线具有积极意义。     

Implementation of MGWR-SAR models for investigating a local particularity of European regional innovation processes

Central European Journal of Operations Research - This study puts emphasis on spatial effects, spatial autocorrelation and heterogeneity in the context of European regional innovation activities....     

中国区域就业结构与区域收入差异的实证研究—基于panel-data模型

运用面板数据模型(Panel-data Model)分析了直辖市、东中西部三大产业的就业结构对区域收入差异的影响,不同区域的就业结构对区域的收入差异影响不同,针对不同区域情况,提出了协调发展产业,调整产业就业结构.促使区域差异缩小的政策建议.     

Nonparametric regression under dependent errors with infinite variance

We consider local least absolute deviation (LLAD) estimation for trend functions of time series with heavy tails which are characterised via a symmetric stable law distribution. The setting includes both causal stable ARMA model and fractional stable ARIMA model as special cases. The asymptotic limit of the estimator is established under the assumption that the process has either short or long memory autocorrelation. For a short memory process, the estimator admits the same convergence rate as if the process has the finite variance. The optimal rate of convergencen ^−2/5 is obtainable by using appropriate bandwidths. This is distinctly different from local least squares estimation, of which the convergence is slowed down due to the existence of heavy tails. On the other hand, the rate of convergence of the LLAD estimator for a long memory process is always slower thann ^−2/5 and the limit is no longer normal.     

Reparameterized and Marginalized Posterior and Predictive Sampling for Complex Bayesian Geostatistical Models

This article proposes a four-pronged approach to efficient Bayesian estimation and prediction for complex Bayesian hierarchical Gaussian models for spatial and spatiotemporal data. The method involves reparameterizing the covariance structure of the model, reformulating the means structure, marginalizing the joint posterior distribution, and applying a simplex-based slice sampling algorithm. The approach permits fusion of point-source data and areal data measured at different resolutions and accommodates nonspatial correlation and variance heterogeneity as well as spatial and/or temporal correlation. The method produces Markov chain Monte Carlo samplers with low autocorrelation in the output, so that fewer iterations are needed for Bayesian inference than would be the case with other sampling algorithms. Supplemental materials are available online.     

Hilbertian spatial periodically correlated first order autoregressive models

In this article, we consider Hilbertian spatial periodically correlated autoregressive models. Such a spatial model assumes periodicity in its autocorrelation function. Plausibly, it explains spatial functional data resulted from phenomena with periodic structures, as geological, atmospheric, meteorological and oceanographic data. Our studies on these models include model building, existence, time domain moving average representation, least square parameter estimation and prediction based on the autoregressive structured past data. We also fit a model of this type to a real data of invisible infrared satellite images.     

空间自相关地理加权回归模型的估计

地理加权回归作为一类能有效处理回归分析中空间非平稳性现象的建模技术,在多类问题的研究得到了广泛的应用.主要讨论这类空间计量经济学模型在空间自相关情形下的估计问题.首先,对于因变量含有空间滞后项的地理加权回归模型,分别给出了局部似然估计和两步估计两种方法.其次,考虑了误差空间自相关下地理加权回归模型的估计问题.     

汇率的就业传导渠道及其效应测算——基于中国制造业面板数据的实证研究

文章研究了汇率的就业传导渠道,及其行业异质性。本文基于传统的生产函数理论,通过建模和理论分析得出:汇率影响就业的传导渠道主要有出口需求、资源配置和效率的三渠道。并基于中国制造业30个行业的2003年至2009年的面板数据,运用残差替代法和面板平滑机制转换回归,测算出各传导渠道在汇率影响就业过程中所起到的作用,结果表明:在整个传导机制中,出口需求对就业的作用较强,效率渠道和资源配置作用相对较弱;人民币升值对就业的总体效应为负效应,且传导机制中各渠道均具不同程度不同特征的行业异质性。     

涟漪效应还是空间互动——基于空间面板联立方程模型的房价与地价关系研究

本文通过构建包含房价和地价在内的空间面板联立方程模型,利用2009～2016年中国286个地市的数据,构建空间面板联立方程模型,以研究房价和地价的空间自相关性和空间溢出效应。实证结果表明：房价与地价之间的空间互动作用明显,房价不但受到当地地价的影响还会受到周边城市房价的影响;而城市地价也会受到当地房价引起的引致需求影响,并且受到周边城市地价的影响。分区域来看,地价对房价的作用强度在东、中、西部地区依次下降,房价对地价作用强度呈现出相反的趋势。而从价格的溢出效应来看,无论是房价还是地价,西部地区的价格溢出效应都最为明显,表明相对于东、中部地区,西部地区城市在房价和地价上都更会受到周围城市的影响。     

Dynamic modelling in development plannin

In this paper a dynamic regional model is proposed, aimed at understanding and forecasting the dynamic response of the population to many different investment policies in the agricultural, services and industrial sectors. In spite of its generality, the model has been conceived in order to be applied to underdeveloped regions. Particularly, from the demographic point of view, attention has been focused on migration phenomena; from the economic point of view, attention has been focused mainly on the rate of growth of the capital stock.

Modularity is another feature of the proposed model; it is made of seven submodels which describe in a very aggregated form the evolution of the regional socio-economic system. The outputs of the submodels are then spatially distributed using a set of ‘spatial operators’ which take into account many social and economic factors relevant to the sub-areas into which the region can be divided.     

Computation of Spatial Covariance Matrices

In an earlier article, Ghosh derived the density for the distance between two points uniformly and independently distributed in a rectangle. This article extends that work to include the case where the two points lie in two different rectangles in a lattice. This density allows one to find the expected value of certain functions of this distance between rectangles analytically or by one-dimensional numerical integration.

In the case of isotropic spatial models or spatial models with geometric anisotropy terms for agricultural experiments one can use these theoretical results to compute the covariance between the yields in different rectangular plots. As the numerical integration is one-dimensional these results are computed quickly and accurately. The types of covariance functions used come from the Matérn and power families of processes. Analytic results are derived for the de Wijs process, a member of both families and for the power models also.

Software in R is available. Examples of the code are given for fitting spatial models to the Fairfield Smith data. Other methods for the estimation of the covariance matrices are discussed and their pros and cons are outlined.     

中国数字经济发展水平:时空特征、动态演化及影响因素

基于构建的数字经济发展水平评价指标体系与2010~2018年全国30个省市(除西藏、港澳台)的面板数据,对中国数字经济发展水平的时空特征、动态演化与区域差异原因进行探究。研究发现:全国及三大区域的数字经济发展水平均呈现稳步上升态势,并伴有明显的空间相关性、空间集聚性和区域差异性,且东部相较于中西部区域内差异更大。数字经济发展速度动态演化显示,整体上中国各省市数字化发展水平变化速度状态表现为上升趋势,相较于中西部,东部拥有更大的速度变化状态。地区前期数字化基础、经济发展的一阶滞后、政府科技投入均会促进区域数字经济的发展,对外开放水平会抑制数字经济发展,地区规模对数字经济的影响表现为非线性。此外,不同线性影响因素在不同区域内对数字经济发展水平具有不同的影响力。     

A Test of Correlation in the Random Coefficients of an Autoregressive Process

A random coefficient autoregressive process in which the coefficients are correlated is investigated. First we look at the existence of a strictly stationary causal solution, we give the second-order stationarity conditions and the autocorrelation function of the process. Then we study some asymptotic properties of the empirical mean and the usual estimators of the process, such as convergence, asymptotic normality and rates of convergence, supplied with appropriate assumptions on the driving perturbations. Our objective is to get an overview of the influence of correlated coefficients in the estimation step through a simple model. In particular, the lack of consistency is shown for the estimation of the autoregressive parameter when the independence hypothesis in the random coefficients is violated. Finally, a consistent estimation is given together with a testing procedure for the existence of correlation in the coefficients. While convergence properties rely on ergodicity, we use a martingale approach to reach most of the results.     

住房价格波动对自有住房家庭消费的区域异质性影响研究

为了解决八大类消费品价格数据不易得到的难题,本文构建投入产出价格模型,分别经验研究25个省(自治区和直辖市)住房价格同时变动不同幅度对八大类消费品价格的影响。25个省(自治区和直辖市)消费品价格变动幅度结果即为QUAIDS模型中的消费品价格变量。然后,本文采用CFPS2010数据,在QUAIDS模型中加入反映家庭所在地区的人口特征变量,研究住房价格波动对自有住房家庭消费的区域异质性影响。研究结果表明住房价格波动对自有住房家庭生存型消费需求区域异质性影响的财富效应显著,而对享受型和发展型消费需求区域异质性影响的挤出效应显著。在此基础上,本文进一步运用补偿变动研究住房价格波动对自有住房家庭福利的区域异质性影响。研究结果表明为了提高自有住房家庭的生活质量和促进社会的和谐稳定,政府应该把稳定东部地区住房价格放在首位。且当遇到如何优化家庭消费结构问题时,政府应该根据地区差异划分市场。     

大学、企业研发与区域创新的空间统计与计量分析

利用空间统计的Moran指数和空间计量经济学的空间误差模型和空间滞后模型,对2000年中国大陆31个省域的创新产出与大学与企业的关系进行了空间计量经济分析。结果发现:31个省域创新能力贡献主要是由企业研究与开发投入实现,大学研发对区域创新能力没有明显的作用,大学与企业研发的结合也没有对区域创新产生明显贡献。     

Maximum-Likelihood Asymptotic Inference for Autoregressive Hilbertian Processes

The autoregressive Hilbertian process framework has been introduced in Bosq (2000). This book provides the nonparametric estimation of the autocorrelation and covariance operators of the autoregressive Hilbertian processes. The asymptotic properties of these estimators are also provided. The maximum likelihood approach still remains unexplored. This paper obtains the asymptotic distribution of the maximum likelihood (ML) estimators of the auto-covariance operator of the Hilbert-valued innovation process, and of the autocorrelation operator of a Gaussian autoregressive Hilbertian process of order one. A real data example is analyzed in the financial context for illustration of the performance of the projection maximum likelihood estimation methodology in the context of missing data.     

对DW检验法的补充和改进

自相关检验是回归分析的一项重要内容，ＤＷ检验法又是自相关检验的最常用方法。但是，我国现有教科书中所介绍的ＤＷ检验都存在两个不确定的区间。对于这两个区间内的自相关如何判定，一直为我国学者所忽视。本文则是针对这一问题，特介绍一种方法，以解决之。     

Multivariate Adaptive Splines for Analysis of Longitudinal Data

Abstract

An essential feature of longitudinal data is the existence of autocorrelation among the observations from the same unit or subject. Two-stage random-effects linear models are commonly used to analyze longitudinal data. These models are not flexible enough, however, for exploring the underlying data structures and, especially, for describing time trends. Semi-parametric models have been proposed recently to accommodate general time trends. But these semi-parametric models do not provide a convenient way to explore interactions among time and other covariates although such interactions exist in many applications. Moreover, semi-parametric models require specifying the design matrix of the covariates (time excluded). We propose nonparametric models to resolve these issues. To fit nonparametric models, we use the novel technique of the multivariate adaptive regression splines for the estimation of mean curve and then apply an EM-like iterative procedure for covariance estimation. After giving a general algorithm of model building, we show how to design a fast algorithm. We use both simulated and published data to illustrate the use of our proposed method.