Definition (1) Econometrics: economic measurement. (2) Econometrics: the social science in which the tools of economic theory, mathematics, and statistical inference are applied to the analysis of economic phenomena. (3) Econometrics: the result of a certain outlook on the role of economics, consists of the application of mathematical statistics to economic data to lend

where a subscribed element of a matrix is always read as arou, column. Here we confine the element to be real number a vector is a matrix with one row or one column. Therefore a row vector is Alxk and a column vector is AixI and commonly denoted as ak and ai,respec- tively. In the followings of this course, we follow conventional custom to say that a vector is a columnvector except for

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

• 回归分析概述 • 双变量线性回归模型的参数估计 • 双变量线性回归模型的假设检验 • 双变量线性回归模型的预测 • 实例

时间序列数据或截面数据都是一维数据。例如时间序列数据是变量按时间得到的数据；截面数据是变量在截面空间上的数据。面板数据是同时在时间和截面上取得的二维数据。所以，面板数据（panel data）也称时间序列截面数据（time series and cross section data）或混合数据（pool data）

3.1 多元线性回归模型 3.2 回归参数的估计 3.3 参数估计量的性质 3.4 回归方程的显著性检验 3.5 中心化和标准化 3.6 相关阵与偏相关系数 3.7 本章小结与评注

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. 18 Vector Time series 1 Introduction In dealing with economic variables often the value of one variables is not only related to its predecessors in time but, in addition, it depends on past values of other variables. This naturally extends the concept of univariate stochastic process to vector time series analysis. This chapter describes the dynamic in

• 一、向量自回归模型定义 • 二、VAR的稳定性 • 三、VAR模型滞后期k的选择 • 四、VAR模型的脉冲响应函数和方差分解 • 五、格兰杰非因果性检验 • 六、VAR与协整 • 七、实例

• 一、多重共线性的概念 • 二、实际经济问题中的多重共线性 • 三、多重共线性的后果 • 四、多重共线性的检验 • 五、克服多重共线性的方法 • 六、案例 • *七、分部回归与多重共线性