从人力资源开发视角探讨教育对国家经济发展的作用

知识经济是当今世界经济发展的最明显特征,为适应知识经济时代的要求,充分发挥教育的主导作用,本文从人力资源是第一资源、保证培养人才的数量、质量以及人力资本投资等方面探讨了教育在国家经济发展中的重要性及作用。     

基于MS-AR模型的中国能源消费周期路径演化识别

文章基于改革开放以来的煤炭、石油、天然气、一次电力及其他能源消费增长率年度数据,运用HP滤波和MS-AR模型,透析能源消费周期多阶段的区制属性,刻画我国能源消费增速的时间路径变化特征。结果表明:(1)除一次电力及其他能源消费增长率外,各能源消费增长率均处于窄幅震荡状态,21世纪初呈现明显的下行态势;煤炭、石油增长率波动程度明显强于天然气、一次电力及其他能源消费增长率波动程度。(2)各能源消费增长率存在低速增长区制、中速增长区制、高速增长区制的三区制特征;以煤炭、石油为代表的传统能源消费增长率在低速和高速增长区制间动态跃迁,仅在相关政策、重大改革方针出台后跃入高速增长区制,在中速增长区制具有持续性;天然气消费增长率在低速增长区制具有惰性;21世纪以来,一次电力及其他能源消费增长率维持在高增长区制。(3)区制特征与波动风险具有联动效应,高(低)增长区制具有高(低)波动风险。     

基于住宅过滤理论对广州城市廉租房建设模式的思考

受到政府资金供给不足影响,广州廉租房建设出现了政策覆盖面窄、廉租社区配套不足等问题。由住宅过滤理论对城市住房保障问题的启示,笔者认为广州不应再采用规模兴建新住宅的方式,而应该引导、促进市场的住房过滤,将市场过滤出的低质量住宅作为向低收入家庭提供廉租住宅的主要渠道。     

基于辅助粒子滤波的DSGE模型识别方法及应用

动态随机一般均衡模型中涵盖无法直接观测的变量,同时跨方程约束涉及复杂的非线性关系使方程的解析估计难以实现。在贝叶斯框架下识别动态随机一般均衡模型,基于状态空间方法建立度量方程和状态转移方程,采用辅助粒子滤波预测条件后验分布,建立贝叶斯误差带描述宏观经济变量脉冲响应函数的动态特征。实际数据分析验证了贝叶斯识别方法的有效性。     

On testes for threshold–type nonlinearity in irregulaly spaced time series

Formulating the model first in continuous time, we have developed a state space approach to the problem of testing for threshold-type nonlinearity when the data are irregularly spaced.     

中国货币政策规则的实证分析——基于麦克勒姆(McCallum)的视角

通过选取1998年1季度至2011年4季度间共56个季度的经济增长、通货膨胀、基础货币增长率来验证麦克勒姆规则对实体经济变动的解释能力。结果表明,中国基础货币的增长基本与麦克勒姆规则模拟值趋势大体一致,虽然在部分期间,实际值与模拟值出现了一定程度的偏差,但是总体趋势没有太大变化。这说明中国的货币政策总体上是遵循麦克勒姆规则的,且对人民银行货币政策松紧程度具有一定的指示作用。     

利用匹配滤波改进MIMO预编码方案

针对多用户多输入多输出(MIMO)系统下行链路的共信道干扰(CCI)问题,提出了一种可以实现发射机和接收机联合设计的预编码方案。该方案假定接收机为匹配滤波器,避免了最小均方误差(MMSE)准则下发射机和接收机联合设计中的迭代操作,同时将迫零处理引入MMSE方法,完全消除了CCI。为进一步简化联合设计问题,对等效信道矩阵进行奇异值分解,将向量优化问题转化为标量优化问题。仿真结果表明,与其他非迭代的预编码方案相比,该联合设计的预编码方案可以获得更好的平均误比特率性能和总容量性能。     

小型化超宽阻带低通滤波器的设计

提出一种小型化超宽阻带低通滤波器设计方法,该方法对常见的高低阻抗低通滤波器进行结构改进。首先将原来直线连接的高阻抗线和低阻抗线变换为成90°直角相连接,利用直角拐角的不连续性产生寄生参量对阻带远端由高次谐波产生的寄生通带进行抑制,极大地缩小了低通滤波器的体积。同时将低通滤波器中的部分传输线用与其等效的T形节替代,实现了带阻滤波器嵌入到低通滤波器内部,既对阻带近端由低次谐波产生的寄生通带进行抑制,又不影响低通滤波器的通带内性能。该低通滤波器性能优越,体积比常见的高低阻抗低通滤波器体积缩小了50%,通带0～4GHz,插入损耗<0.5dB,超宽阻带(5个倍频程)5～30GHz,抑制>40dB。     

Multivariate non-linear time series modelling of exposure and risk in road safety research

Summary.  A multivariate non-linear time series model for road safety data is presented. The model is applied in a case-study into the development of a yearly time series of numbers of fatal accidents (inside and outside urban areas) and numbers of kilometres driven by motor vehicles in the Netherlands between 1961 and 2000. The model accounts for missing entries in the disaggregated numbers of kilometres driven although the aggregated numbers are observed throughout. We consider a multivariate non-linear time series model for the analysis of these data. The model consists of dynamic unobserved factors for exposure and risk that are related in a non-linear way to the number of fatal accidents. The multivariate dimension of the model is due to its inclusion of multiple time series for inside and outside urban areas. Approximate maximum likelihood methods based on the extended Kalman filter are utilized for the estimation of unknown parameters. The latent factors are estimated by extended smoothing methods. It is concluded that the salient features of the observed time series are captured by the model in a satisfactory way.     

Parameter Estimation for a Bidimensional Partially Observed Ornstein–Uhlenbeck Process with Biological Application

Abstract. We consider a bidimensional Ornstein–Uhlenbeck process to describe the tissue microvascularization in anti‐cancer therapy. Data are discrete, partial and noisy observations of this stochastic differential equation (SDE). Our aim is to estimate the SDE parameters. We use the main advantage of a one‐dimensional observation to obtain an easy way to compute the exact likelihood using the Kalman filter recursion, which allows to implement an easy numerical maximization of the likelihood. Furthermore, we establish the link between the observations and an ARMA process and we deduce the asymptotic properties of the maximum likelihood estimator. We show that this ARMA property can be generalized to a higher dimensional underlying Ornstein–Uhlenbeck diffusion. We compare this estimator with the one obtained by the well‐known expectation maximization algorithm on simulated data. Our estimation methods can be directly applied to other biological contexts such as drug pharmacokinetics or hormone secretions.