Stationary Stochastic Process e A stochastic process is stationary if for every collection of time indices 11 e Thus, stationarity implies that the x,'s are dentically distributed and that the nature of any correlation between adjacent terms is

Functional form e We' ve seen that a linear regression can really fit nonlinear relationships 2 Can use logs on RHS, LHS or both Can use quadratic forms ofx's Can use interactions ofx's e How do we know if we've gotten the right functional form for our model? Economics 20- Prof anderson

一、序列相关性的概念——违反基本假设的定义及违反的原因 二、序列相关性的后果——违反基本假设会造成什么样的后果 三、序列相关性的检验——怎样诊断是否违反基本假设 四、具有序列相关性模型的估计——如何消除或减弱对基本假设的违反 五、案例

一、一元线性回归模型的基本假设 二、参数的普通最小二乘估计（OLS） 三、参数估计的最大或然法(ML) 四、最小二乘估计量的性质 五、参数估计量的概率分布及随机干扰项方差的估计

Parallels with Simple regression Bo is still the intercept B, to Bk all called slope parameters u is still the error term(or disturbance) Still need to make a zero conditional mean assumption, so now assume that E(lx,x2…,x)=0 Still minimizing the sum of squared residuals. so have k+l first order conditions Economics 20- Prof anderson

Assumptions of the classical Linear Model (Clm) e So far, we know that given the Gauss Markov assumptions, OLS IS BLUE e In order to do classical hypothesis testing we need to add another assumption(beyond the Gauss-Markov assumptions) Assume that u is independent of x,x2…,xk and u is normally distributed with zero mean and variance 0: u- Normal(0, 02) Economics 20- Prof anderson

一、简单线性回归模型的设定 二、简单线性回归模型的基本假定 三、简单线性回归模型参数的估计方法 四、参数估计量的统计性质 五、拟合优度的度量 六、回归系数的区间估计和假设检验 七、回归模型预测 八、EViews应用

Redefining variables Changing the scale of the y variable will lead to a corresponding change in the scale of the coefficients and standard errors. so no change in the significance or interpretation Changing the scale of one x variable will lead to a change in the scale of that coefficient and standard error, so no change in the significance or interpretation Economics 20- Prof anderson

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