农民人均纯收入与农业生产结构的关系研究

基于改革开放以来的宏观数据,运用SVAR模型主要进行脉冲响应分析、方差分析。结果显示,农民人均纯收入与农业生产结构的几个指标之间存在长期稳定的均衡关系,农业(种植业)产值对农民人均纯收入的影响最大,农民人均纯收入对农业生产结构变动的响应具有一定的滞后性。因此,提出转变发展思想,以工业的形式发展农业;推进产业改革,调优农业生产结构;调整产业政策,增添农民人均纯收入增长动力。     

2001～2010年中国植被NPP的时空变化及其与气候的关系

基于2001~2010年全国气象数据及MODIS NDVI数据,利用改进的光能利用率模型( CASA模型)反演了全国植被NPP时空格局,并分析了其与年平均温度及降雨量的关系,结果表明：(1)近10年全国植被平均NPP空间分布地域性明显,总体上北低南高,由西北向东南逐渐增加。其中西北沙漠地区的植被NPP值最小,大都在100gC/m2以下。南方大部分地区植被NPP值在1000gC/m2以上,特别是广西、广东及海南等省份更是达到了1500gC/m2以上。(2)近10年全国植被平均NPP总体上呈逐年增加的趋势,倾斜率约为2．82gC/m2·a,即每年每平方米增加的NPP约为2．82gC。相关系数为0．552,未达显著水平。(3)近10年来全国大部分地区的年平均NPP与年平均温度之间呈正相关,这部分地区占所有地区的比例为75．24%。(4)近10年全国绝大部分地区的年平均NPP与降雨量之间呈正相关,这部分地区占所有地区的比例为99．91%,说明降雨量对植被NPP的影响大于年均温。     

Mining big data using parsimonious factor,machine learning,variable selection and shrinkage methods

A number of recent studies in the economics literature have focused on the usefulness of factor models in the context of prediction using “big data” (see Bai and Ng, 2008; Dufour and Stevanovic, 2010; Forni, Hallin, Lippi, & Reichlin, 2000; Forni et al., 2005; Kim and Swanson, 2014a; Stock and Watson, 2002b, 2006, 2012, and the references cited therein). We add to this literature by analyzing whether “big data” are useful for modelling low frequency macroeconomic variables, such as unemployment, inflation and GDP. In particular, we analyze the predictive benefits associated with the use of principal component analysis (PCA), independent component analysis (ICA), and sparse principal component analysis (SPCA). We also evaluate machine learning, variable selection and shrinkage methods, including bagging, boosting, ridge regression, least angle regression, the elastic net, and the non-negative garotte. Our approach is to carry out a forecasting “horse-race” using prediction models that are constructed based on a variety of model specification approaches, factor estimation methods, and data windowing methods, in the context of predicting 11 macroeconomic variables that are relevant to monetary policy assessment. In many instances, we find that various of our benchmark models, including autoregressive (AR) models, AR models with exogenous variables, and (Bayesian) model averaging, do not dominate specifications based on factor-type dimension reduction combined with various machine learning, variable selection, and shrinkage methods (called “combination” models). We find that forecast combination methods are mean square forecast error (MSFE) “best” for only three variables out of 11 for a forecast horizon of $h=1$, and for four variables when $h=3$ or $12$. In addition, non-PCA type factor estimation methods yield MSFE-best predictions for nine variables out of 11 for $h=1$, although PCA dominates at longer horizons. Interestingly, we also find evidence of the usefulness of combination models for approximately half of our variables when $h>1$. Most importantly, we present strong new evidence of the usefulness of factor-based dimension reduction when utilizing “big data” for macroeconometric forecasting.     

Probabilistic forecasting in day-ahead electricity markets: Simulating peak and off-peak prices

In this article we include dependency structures for electricity price forecasting and forecasting evaluation. We work with off-peak and peak time series from the German-Austrian day-ahead price; hence, we analyze bivariate data. We first estimate the mean of the two time series, and then in a second step we estimate the residuals. The mean equation is estimated by ordinary least squares and the elastic net, and the residuals are estimated by maximum likelihood. Our contribution is to include a bivariate jump component in a mean reverting jump diffusion model in the residuals. The models’ forecasts are evaluated with use of four different criteria, including the energy score to measure whether the correlation structure between the time series is properly included. It is observed that the models with bivariate jumps provide better results with the energy score, which means that it is important to consider this structure to properly forecast correlated time series.     

DNS服务应用于小型网络教学的研究

网络教学已成为世界各国教育改革和发展的重要趋势,小型网络的DNS服务因其可以在实验室中模拟互联网DNS服务的作用,使学生从全局上理解DNS服务,在网络教学中具有切实可行的意义。该系统已经实现了互联网上DNS服务器的基本功能,可以较好地工作,并可以比较方便地进行系统扩充。     

杉木人工林经济成熟龄的研究

利用杉木人工林标准地材料建立生长收获预估模型,根据技术经济指标采用净现值法研究经济成熟。结果表明:在现有技术经济指标的情况下,利率为5%时杉木人工林经济成熟龄为17年。若其它条件保持不变,木材价格或成本每增减20%,杉木人工林经济成熟龄将推迟或提前1年。利率对经济成熟龄的到来有显著影响,利率每增加1个百分点,经济成熟龄将提前1年。     

供应链拓扑结构模型研究

从企业与企业之间关系的角度提出了供应链的拓扑结构模型：链状模型、网状模型和石 墨模型,相应地给出了供应链的子网、级、入点、出点等概念。针对不同的研究对象和研究阶段而提出 的供应链概念和模型有助于对供应链的认识和研究,对国有企业重组有启示意义。     

知识经济与人本管理

路演是一种新兴的股票推介方式。网上路演采用了传统路演的核心思想 ,并借助互联网络的优势 ,可以较好地实现投资者和发行者之间沟通 ,并对股票正常发行起到一定促进作用。网上路演对于促进我国证券事业的发展 ,更好地展示上市公司形象具有积极的意义。     

关于网络图书馆的思考

网络时代的图书馆必须敢于创新。注意分析用户的借阑行为．列用丰富的信息与便捷的服务吸引用户,利用网络技术与网络优势确立图书馆的地位．韭视全文数据库的建设,积极调整文献信息资源结构．通过多种途径收集网络上的动态佶息．更好地为用户服务。     

On the Choice Between the Stocking Rate and Time in Range Management

A long standing question in range management concerns the relative importance of the stocking rate versus the length oftime during which animals graze a particular rangeland. Weaddress this question by analyzing the problem faced by a privaterancher who wishes to minimize the long run expected net unit cost (LRENC) from range operations by choosing either the stocking rate or the length of time during which his animals graze hisrangeland. We construct a renewal-theoretic model and show that,in general, this rancher's LRENC with an optimally chosen stocking rate is lower than his LRENC with an optimally chosen grazing cycle length. From a management perspective, this means that correct stocking of the range is more important than the length of time during which animals graze the range. In addition, our research shows how to address questions concerning the desirability of temporal versus non-temporal controls in managing naturalresources such as fisheries and hunting grounds.