空间分析源于60年代地理和区域科学的计量革命,在开始阶段,主要是应 用定量(主要是统计)分析手段用于分析点线、面的空间分布模式。后来更多的 是强调地理空间本身的特征、空间决策过程和复杂空间系统的时空演化过程分析。 实际上自有地图以来,人们就始终在自觉或不自觉地进行着各种类型的空间分析。 如在地图上量测地理要素之间的距离、方位、面积,乃至利用地图进行战术研究和 战略决策等,都是人们利用地图进行空间分析的实例,而后者实质上已属较高层次 上的空间分析

第一节长期负债及计量原理 第二节长期借款 第三节应付债券 第四节可转换债券 第五节其他长期负债 第六节债务重组

以SSC收录的1996一2017年科学推理研究的200篇文献为研究对象,运用HistCite和CiteSpace软件,从科学计量学的角度对文献数据进行可视化分析

⚫ 第一节 数据的计量与类型 ⚫ 第二节 统计数据搜集的组织形式 ⚫ 第三节 统计数据搜集的方法 ⚫ 第四节 统计调查方案 ⚫ 第五节 统计数据的整理与显示

Ch. 11 Panel Data model Data sets that combine time series and cross sections are common in econo- metrics. For example, the published statistics of the OECD contain numerous series of economic aggregate observed yearly for many countries. The PSID is a studies of roughly 6000 families and 15000 individuals who has been interviews periodically from 1968 to the present

Ch. 13 Difference Equations 1 First-Order Difference Equations Suppose we are given a dynamic equation relating the value y takes on at date t to another variables Wt and to the value y took in the previous period: where o is a constant. Equation(1)is a linear first-order difference equation a difference equation is an expression relating a variable yt to its previous values

Ch. 15 Forecasting Having considered in Chapter 14 some of the properties of ARMA models, we now show how they may be used to forecast future values of an observed time series. For the present we proceed as if the model were known ecactly Forecasting is an important concept for the studies of time series analysis. In the scope of regression model we usually

Ch. 17 Maximum likelihood estimation e identica ation process having led to a tentative formulation for the model, we then need to obtain efficient estimates of the parameters. After the parameters have been estimated, the fitted model will be subjected to diagnostic checks This chapter contains a general account of likelihood method for estimation of the parameters in the stochastic model

Ch. 19 Models of Nonstationary Time Series In time series analysis we do not confine ourselves to the analysis of stationary time series. In fact, most of the time series we encounter are nonstationary. How to deal with the nonstationary data and use what we have learned from stationary model are the main subjects of this chapter 1 Integrated Process

Ch. 20 Processes with Deterministic Trends 1 Traditional Asymptotic Results of OlS Suppose a linear regression model with stochastic regressor given by Y=x!3+e,t=1,2,…,T,;B∈R or in matrix form y=xB+E We are interested in the asymptotic properties such as consistency and limiting