Consistency e Under the Gauss-Markov assumptionS OLS IS BLUE, but in other cases it wont always be possible to find unbiased estimators o In those cases, we may settle for estimators that are consistent, meaning as n→>∞,the distribution of the estimator collapses to the parameter value Economics 20- Prof anderson

One of the CLRM assumptions is: there is no perfect multicollinearity-no exact linear relationships among explanatory variables, Xs, in a multiple regression. In practice, one rarely encounters perfect multicollinearity, but cases of near or very high multicollinearity where explanatory variables are approximately linearly related frequently arise in many applications

9.1 向量自回归理论 9.2 结构VAR(SVAR)模型的识别条件 9.3 VAR模型的检验 9.4 脉冲响应函数 9.5 方差分解 9.6 Johansen协整检验 9.7 向量误差修正模型(VEC)

14.1 Restricted Least Squares (RLS) 1. OLS and RLS ()Unrestricted least squares(ULS) When using the ordinary least square method(OLS) to estimate the parameters, we do not put any prior constraint() or restriction(s) on the parameters. So we can estimate the parameters without any restrictions. This is ULS

Single equation regression models: -The dependent variable, Y, is expressed as a linear function of one or more explanatory variables, the Xs. Assumption the cause-and-effect relationship, if any, between Y and the Xs is unidirectional: explanatory variables are the cause; the dependent variable is the effect

• 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

12.1 The Nature of Autocorrelation 1. Definition (1) CLRM assumption: No autocorrelation exist in dishurbances ui; E(iμi)=0 Autocorrelation means: E(μiμ)≠0 (2) Autocorrelation is usually associated with time series data, but it can also occur in cross-sectional data, which is called spatial correlation

The models we discussed are models that are linear in parameters; variables Y and Xs do not necessarily have to be linear The price elasticity of demand~the log-linear models The rate of growth~semilog model Functional forms of regression models which are linear in parameters, but not necessarily linear in variables: