sas中进行异方差调整的方法

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好像不是很多，除了知道异方差形式的，还有就是广义矩估计GMM，少了一个经常在paper中看到的

White Heteroskedasticity-Consistent Standard Errors & Covariance，

还没发现SAS中有这样调整异方差的，大家有发现了吗？

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关键词：异方差 covariance consistent variance Standard 方差 调整 SAS

https://bbs.pinggu.org/thread-140757-1-1.html

这个是我在版上跟你搜索一样的问题的时候找到的一个人的帖子，因为自己的基础不是很好，所以不是很理解，希望对你有帮助：）

另外 我也在找关于稳健性检验的东西 有的话希望能共享哈，多谢：）

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SAS CODE FOR HETEROSCEDASTICITY


libname w

'c:\Documents and Settings\ApecUser\My Documents\Teaching\ApEc5031\Wooldridge Datasets\';


proc reg data=w.hprice1;

model lprice=lassess llotsize lsqrft bdrms;
run;

data hprice1;

set w.hprice1;

diff=lprice-lassess;
run;

proc reg data=hprice1;

model diff=;
run;




proc reg data=w.hprice1;

model lprice=lassess llotsize lsqrft bdrms;

test lassess=1, llotsize=0, lsqrft=0, bdrms=0;
run;


proc reg data=w.hprice1;

model lprice=lassess llotsize lsqrft bdrms/acov;

test lassess=1, llotsize=0, lsqrft=0, bdrms=0;
run;




proc reg data=w.hprice1;

model lprice=lsqrft;

output out=resid


p=lprhat


r=uhat;

run;
proc reg data=resid;

model llotsize=lsqrft;

output out=resid1


p=lsqfthat



r=r1hat;

run;
proc reg data=resid1;

model bdrms=lsqrft;

output out=resid2


p=bdrmhat


r=r2hat;

run;
data final;

set resid2;

r1u=r1hat*uhat;


r2u=r2hat*uhat;

one=1;

run;
proc reg data=final;

model one=r1u r2u/noint;
run;



proc reg data=w.hprice1;

model lprice=llotsize lsqrft bdrms;

output out=resid


p=lprhat


r=uhat;
run;
data resid2;

set resid;

u2=uhat*uhat;
run;
proc reg data=resid2;

model u2=llotsize lsqrft bdrms;
run;





proc reg data=w.hprice1;

model lprice=llotsize lsqrft bdrms;

output out=resid


p=lprhat


r=uhat;
run;
data resid2;

set resid;

u2=uhat*uhat;

lprhat2=lprhat*lprhat;

lot2=llotsize*llotsize;

sq2=lsqrft*lsqrft;

bd2=bdrms*bdrms;

lot_sq=llotsize*lsqrft;

bd_sq=bdrms*lsqrft;

lot_bd=llotsize*bdrms;
run;
proc reg data=resid2;

model u2=llotsize lsqrft bdrms lot2 sq2 bd2 lot_sq bd_sq lot_bd;
run;



proc reg data=resid2;

model u2=lprhat lprhat2;
run;



proc reg data=w.hprice1;

model lprice=llotsize lsqrft bdrms/spec;
run;





proc reg data=w.saving;

title "OLS run";

model sav=inc size educ age black;

output out=resid


p=lprhat


r=uhat;
run;


data saving;


set w.saving;

adjsav=sav/sqrt(inc);

adjint=1/sqrt(inc);

adjinc=inc/sqrt(inc);

adjsize=size/sqrt(inc);

adjeduc=educ/sqrt(inc);

adjage=age/sqrt(inc);

adjblack=black/sqrt(inc);
run;
proc reg data=saving;

title "Manual WLS";

model adjsav=adjint adjinc adjsize adjeduc adjage adjblack/noint;
run;




data saving;

set w.saving;

wght=1/inc;
run;
proc reg data=saving;

title "Automatic WLS";

model sav=inc size educ age black;

weight wght;
run;




proc reg data=w.saving;

title "White's errors without weights";

model sav=inc size educ age black/acov;
run;
proc reg data=saving;

title "White's errors with weights";

model sav=inc size educ age black/acov;

weight wght;
run;






proc reg data=w.saving;

title "Saving OLS residuals";

model sav=inc size educ age black;

output out=resid


r=uhat


p=yhat;
run;

data resid2;

set resid;

logu2=log(uhat*uhat);

yhat2=yhat*yhat;
run;

proc reg data=resid2;

title "Calculating ghat";

model logu2=inc size educ age black;

output out=fitted


p=ghat;
run;

data fitted2;

set fitted;

hhat=exp(ghat);

wght=1/hhat;
run;

proc reg data=fitted2;

title "feasible GLS";

model sav=inc size educ age black;

weight wght;
run;




proc reg data=resid2;

title "Calculating ghat=f(yhat, yhat2)";

model logu2=yhat yhat2;

output out=fitted


p=ghat;
run;

data fitted2;

set fitted;

hhat=exp(ghat);

wght=1/hhat;
run;

proc reg data=fitted2;

title "feasible GLS with ghat=f(yhat,yhat2)";

model sav=inc size educ age black;

weight wght;
run;

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