Are the standard errors from PROC SURVEYREG White SE?

[somebody]

I am trying to compute White standard errors (SE) following this post http://pages.stern.nyu.edu/~adesouza/comp/sas.html

What I notice is that PROC SURVEYREG provide the same SE as the other options. So is it true that PROC SURVEYREG produces White SE as default? or am I missing anything here? I also test on other datasets and got the same results. Here are the sample dataset and code:

*Get the dataset;
	filename foo1 URL "http://www.kellogg.northwestern.edu/faculty/petersen/htm/papers/se/test_data.txt";
	data ds;
	  infile foo1;
	  input firmid year x y;
	run;
*Now run different methods;
	*Normal SE;
		proc reg data=ds;
		    model y = x;
			ods output ParameterEstimates=temp0 fitstatistics=fit0;
		run;
	*option1: Using PROC SURVEYREG with no special specs;
		proc surveyreg data=ds;
			model y = x / solution;
			ods output ParameterEstimates=temp1 fitstatistics=fit1;
		run;quit;

	*option2;
		proc reg data=ds ;
			model y = x / white;
			ods output ParameterEstimates=temp2 fitstatistics=fit2;
		run;quit;
	*option3;
		proc model data=ds;
		   instruments x ;
		   y=b0+b1*misp + b2*iv + b3*mom6;
			*y = b0 + b1* x;
		   fit y / gmm kernel=(bart,1,0);
		   ods output ParameterEstimates=temp3 fitstatistics=fit3;
		run;quit;
	*option4: same as option 1 but cluster each obs. Dont know why but the article suggests so;
		data ds;
		   set ds;
		   counter=_n_;
		run;
		proc surveyreg data=ds;
		   cluster counter;
		   model y=x;
		   ods output ParameterEstimates=temp4 fitstatistics=fit4;
		run;quit;
	*option5: note that the ods output statement does not work. check the output on Results Viewer;
		proc genmod data=ds;
		   class counter;
		   model y=x;
		   repeated subject=counter /type=ind;
			ods output ParameterEstimates=temp5 fitstatistics=fit5;
		run;

[Zard · Accepted solution]

PROC SURVEYREG uses the Taylor series linearization method to compute the variance. This robust variance estimation method is also referred to as the Huber-White sandwich estimator, the design-based estimator and the Rogers estimator.