空间集聚、区位选择与江苏省外商直接投资:空间计量实证研究

定量概述江苏省外商直接投资发展历程,根据外商直接投资区位选择的区位成本、国际贸易、产业组织和邓宁折衷理论,从成本、经济、集聚、制度等方面,运用1999-2010年FDI数据,对江苏FDI区位选择影响因素进行空间计量实证分析,研究结果表明:1)江苏FDI发展差异总体趋势是绝对差异在逐年扩大,相对差异呈缓慢下降;2)江苏FDI空间趋于非均衡分布,"核心-外围"空间格局有所改变;3)集聚程度、经济发展水平、工业化水平、基础设施、劳动成本是江苏FDI区位选择的决定性影响因素;4)外资产出率、城市化水平、人力资本、对外开放度等负相关影响因素在江苏FDI区位选择中的重要程度显著性下降.江苏应根据后金融危机时代全球FDI新的发展趋势和特征,积极承接国际产业转移,全面参与全球产业分工,加快引导外商投资重点投向现代农业、现代服务业、新兴产业、高新技术产业等领域,优化外资投资方式、投资结构和地区分布.     

一类点边同时变化的增长网络的度分布

本文研究一个节点和连边能同时发生变化的网络模型,把模型中的节点度的演化过程看成一族马氏链。从模型的演化机制中得到该马氏链的状态转移概率,用马氏链的方法证明了该模型的稳态度分布是存在的,并得到了度分布的精确表达式。从而说明了该网络是标度指数为3的无标度网络。     

概率流的分解定理

钱敏平在[2]中,给出了马氏链的可逆与环流分解定理:可数状态空间E上定义的马氏链,若具有平稳的初始分布,则其转移概率P可分解为     

马氏链集间转移的一类强大数定律

本文研究了马氏链从一个状态子集到另一个状态子集的转移概率的极限性质.利用Doob鞅收敛定理,获得了任意随机序列的强大数定律、马氏链泛函的强大数定律和强遍历定理.推广了马氏链传统转移概率的极限性质和强极限定理.     

马氏环境中树指标马氏链随机转移概率调和平均的强极限性质

本文研究了离散状态下随机环境中树指标马氏链,证明了该过程在概率空间中可以实现,同时阐明了马氏环境下树指标马氏链与树指标马氏双链的等价性.获得了有限状态下马氏环境中树指标马氏链的随机转移概率调和平均的强极限性质.     

多状态马氏链的极限定理

本文首先对具有平稳转移概率的有限状态整值马氏链［Ｘｉ］的和Ｓｎ给出了它的概率母函数的一般表达式。利用这一结果，对于二状态马氏链，在很一般的条件下证明了Ｓｎ的分布收敛于几何型分布和复合泊松分布的卷积，较强意义下的收敛性也是被讨论的，对于多状态链，某些特殊情形的极限分布是被给出的。     

外商直接投资对国内投资挤入挤出效应的区制动态分析

本文以1999年到2013年的外商直接投资和国内投资的季度数据为基础,采用MS-VECM模型对我国外商直接投资与国内投资的区制状态、区制相关性以及转移概率进行了实证分析。结果表明,在整个样本期内,外商直接投资对我国的国内投资总体上存在挤入效应,但从动态时间路径上看,在经济运行的不同时期,外商直接投资对我国国内投资的影响并不一致,在1999年一季度至2005年三季度期间表现为对国内投资的挤出效应,而在2005年四季度至2013年四季度期间则表现为挤入效应     

基于灰色马尔可夫模型的物流配送需求量预测研究

研究可以较准确地预测物流配送需求量的数学模型,以服务于政府及企业的物流规划及其决策.将灰色理论和离散状态的马尔可夫链相结合,用灰色马尔可夫链对物流配送需求量进行实证研究.针对灰色数据系列首先用GM模型进行趋势预测,然后利用马尔可夫状态转移概率矩阵预报方法对其预测值进行二次拟合,预测精度明显高于GM模型预测.     

树上路径过程的随机路径条件概率的强极限定理

本文研究了树上路径过程随机转移概率和状态序偶出现频率的强极限定理.通过利用若干重要不等式,获得了树上路径过程的随机路径条件概率用不等式表示的几何平均强极限定理以及树上路径过程关于状态序偶出现频率的用不等式表示的强极限定理,所得结果推广了树上马氏链及非齐次马氏链中的结果.     

我国外商直接投资与经济增长的动态关系:基于面板协整研究

利用面板协整方法和面板误差修正模型对1986～2008年问我国28省市外商直接投资与经济增长关系进行实证分析.结果表明,从长期来看经济增长的调整系数为正,表明外商直接投资与经济增长之间存在长期均衡关系,中国的经济增长会促进外商直接投资增加;但FDI与经济增长的这种长期均衡关系在地区间存在显著差异.     

Markov chain models for delinquency: Transition matrix estimation and forecasting

A Markov chain is a natural probability model for accounts receivable. For example, accounts that are ‘current’ this month have a probability of moving next month into ‘current’, ‘delinquent’ or ‘paid‐off’ states. If the transition matrix of the Markov chain were known, forecasts could be formed for future months for each state. This paper applies a Markov chain model to subprime loans that appear neither homogeneous nor stationary. Innovative estimation methods for the transition matrix are proposed. Bayes and empirical Bayes estimators are derived where the population is divided into segments or subpopulations whose transition matrices differ in some, but not all entries. Loan‐level models for key transition matrix entries can be constructed where loan‐level covariates capture the non‐stationarity of the transition matrix. Prediction is illustrated on a $7 billion portfolio of subprime fixed first mortgages and the forecasts show good agreement with actual balances in the delinquency states. Copyright © 2010 John Wiley & Sons, Ltd.     

用吸收马尔可夫链估算药物在体内的平均转运时间

药物在体内的各种平均时间是药物动力学研究的重要指标。通过一种新的矩阵运算定义了导出矩阵,从而解决了药物速率常数转化为转移概率的问题,推导了药物在体内动态过程的吸收马尔可夫链典范式矩阵和基本矩阵,从基本矩阵可得药物在体内各部位吸收、转运和消除的平均时间的计算公式。     

随机有序马尔可夫链收敛速度显式界的注记

弱化Scott与Tweedie在计算马氏链收敛速度界时的条件,即变一步转移概率为m(m≥1)步转移概率,并运用不同于Scott与Tweedie的方法,计算出马氏链几何收敛速度r~n的界,从而推广了已有的结论.     

Exponential bounds for discrete-time singularly perturbed Markov chains

This paper develops exponential type upper bounds for scaled occupation measures of singularly perturbed Markov chains in discrete time. By considering two-time scale in the Markov chains, asymptotic analysis is carried out. The cases of the fast changing transition probability matrix is irreducible and that are divisible into l ergodic classes are examined first; the upper bounds of a sequence of scaled occupation measures are derived. Then extensions to Markov chains involving transient states and/or nonhomogeneous transition probabilities are dealt with. The results enable us to further our understanding of the underlying Markov chains and related dynamic systems, which is essential for solving many control and optimization problems.     

Censoring,Factorizations, and Spectral Analysis for Transition Matrices with Block-Repeating Entries

In this paper, we use the Markov chain censoring technique to study infinite state Markov chains whose transition matrices possess block-repeating entries. We demonstrate that a number of important probabilistic measures are invariant under censoring. Informally speaking, these measures involve first passage times or expected numbers of visits to certain levels where other levels are taboo; they are closely related to the so-called fundamental matrix of the Markov chain which is also studied here. Factorization theorems for the characteristic equation of the blocks of the transition matrix are obtained. Necessary and sufficient conditions are derived for such a Markov chain to be positive recurrent, null recurrent, or transient based either on spectral analysis, or on a property of the fundamental matrix. Explicit expressions are obtained for key probabilistic measures, including the stationary probability vector and the fundamental matrix, which could be potentially used to develop various recursive algorithms for computing these measures.     

Mover‐stayer model with covariate effects on stayer's probability and mover's transitions

A discrete time Markov chain assumes that the population is homogeneous, each individual in the population evolves according to the same transition matrix. In contrast, a discrete mover‐stayer (MS) model postulates a simple form of population heterogeneity; in each initial state, there is a proportion of individuals who never leave this state (stayers) and the complementary proportion of individuals who evolve according to a Markov chain (movers). The MS model was extended by specifying the stayer's probability to be a logistic function of an individual's covariates but leaving the same transition matrix for all movers. We further extend the MS model by allowing each mover to have her/his covariates dependent transition matrix. The model for a mover's transition matrix is related to the extant Markov chains mixture model with mixing on the speed of movement of Markov chains. The proposed model is estimated using the expectation‐maximization algorithm and illustrated with a large data set on car loans and the simulation.     

BMAP的轨道分析和Q过程的伴随MAP

本文用轨道分析方法研究批量Markov到达过程(BMAP),有别于研究BMAP常用的矩阵解析方法.通过BMAP的表现(D_k,k=0,1,2,…),得到BMAP的跳跃概率,证明了BMAP的相过程是时间齐次Markov链,求出了相过程的转移概率和密度矩阵.此外,给定一个带有限状态空间的Q过程J,其跳跃点的计数过程记为N,证明了Q过程J的伴随过程X*=(N,J)是一个MAP,求出了该MAP的转移概率和表现(D_0,D_1),它们是通过密度矩阵Q来表述的.     

On One Property of a Regular Markov Chain

We prove that if a certain row of the transition probability matrix of a regular Markov chain is subtracted from the other rows of this matrix and then this row and the corresponding column are deleted, then the spectral radius of the matrix thus obtained is less than 1. We use this property of a regular Markov chain for the construction of an iterative process for the solution of the Howard system of equations, which appears in the course of investigation of controlled Markov chains with single ergodic class and, possibly, transient states.     

基于修正转移概率矩阵的Markov方法在企业人才结构预测中的应用

预测企业人才结构的变化;修正转移概率矩阵的Markov预测方法;用修正转移概率矩阵的Markov模型针对人才结构进行定量预测与分析,对历史数据样本数量要求不多,并且计算简便;获取的计算结果更具客观性、真实性.