W06lecture05专题讨论北大光华计量.pptx

第五章．回归模型的专题讨论;Outline;第一节、数据问题;1.多重共线性;后果：精度、稳定性 因为参数估计方差为 Informal assessment of Multicollinearity (Signs of collinearity): rules of thumb Correlation matrix of regressors, 0.75 (say) Contradiction between t-and F-statistics t-statistics individual coefficient(s) not significant F-statistic coefficient(s) jointly significant Take out one variable and re-estimate, big changes in the value of coefficient(s) Formal assesment: 方差膨胀因子 VIF=1/(1-R2) , 一般认为10没问题 proc reg data=one; model eret=beta beta2/vif; run;;What to do about multicollinearity Using prior information to explicitly restrict the parameters Sample problem: get more data! multicollinearity = problem of lack of data Time series: 1st differencing the data 1st differences usually less correlated Drop some control variables But do not drop key variables! –Omitted variables problem Ridge regression Principal components Form linear combination of correlated variables such that it maximizes its variability Drawback: may lack economic reasoning Do nothing if it is significant and stable (Woodridge) 做一些估计对数据稳健性的检验，比如分成几个小样本、去掉几个观察值等。;2. 奇异值问题;PROC IMPORT OUT=two DATAFILE= F:\current_teaching\econometrics_II_2\data\FS_Combas1 DBMS=EXCEL REPLACE; SHEET=FS_Combas1; GETNAMES=YES; MIXED=YES; USEDATE=YES; RUN; PROC IMPORT OUT=one DATAFILE= F:\current_teaching\econometrics_II_2\data\FS_Combas DBMS=EXCEL REPLACE; SHEET=FS_Combas; GETNAMES=YES; MIXED=YES; USEDATE=YES; RUN; proc append base=one data=two; run; data two; set one; lev=A002100000/A001100000; year=substr(accper, 1,4); mth=substr(accper, 6,2); if lev-10000 mth=12; keep stkcd lev year mth; run; proc sort data=two nodupkey; by stkcd year; run; proc means mean std min p1 median p99 max data=two; var lev; run; 产生的原因： 可能是由于数据采集错误 可能是上帝疯狂了几次 ;这些奇异值，会导致模型的估计不稳定。比如以下Monte Carlo 试验：exer3 %let n=100; %let alpha=0.5; %let beta=0.02; %let sigma=0.02; data one(drop=seed1 seed2 seed3); retain seed1 4500; retain seed2 4400; do id=1 t