截面和面板数据分析3.ppt

Analysis of Cross Section and Panel Data Yan Zhang School of Economics, Fudan University CCER, Fudan University Introductory Econometrics A Modern Approach Yan Zhang School of Economics, Fudan University CCER, Fudan University Analysis of Cross Section and Panel Data Part 1. Regression Analysis on Cross Sectional Data Chap 4. Multiple Regression Analysis：Inference E., Var., BLUE the full sample distribution of the OLS estimators Assumption 6 (Normality): Assumption 6 Assumption 3 and 5 (zero conditional mean; homogenous) 古典假设Classical Linear Model (CLM) Assumptions: Assumption 1－6 The Efficiency of the OLS estimators The Efficiency of the OLS estimators under different assumptions: Gauss-Markov Assumptions: BLUE, minimum variance linear unbiased estimators CLM Assumptions: minimum variance unbiased estimators (among all, not only linear estimators in the yi) The Population Assumptions of the CLM: Assumption 6 Given x, the distribution of y is normal Clearly wrong e.g. narr86; prate How? The Normality of the OLS estimators Normality of u normal sampling distributions of the OLS estimators: Therefore, standard normal random v.: More results: any linear combination of the is also normally distributed, and any subset of the has a joint normal distribution. Testing Hypothesis Whether Normality of u Can Be Assumed? Empirical matter: transformation, log(price) CLT(中心极限定理) : Non-normality of the errors is not a serious problem with large sample sizes. Even though the yi are not from a normal distribution, the OLS estimators are approximately normally distributed, at least in large sample sizes. （Chap. 5. ） 仅需Gauss-Markov假设；有限方差、同方差、零条件均值 Whether normality of u can be assumed? And Other Topics about Large Sample Finite sample properties: unbiasedness; BLUE Large sample properties: the asymptotic properties Consistency