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somebody
Lapis Lazuli | Level 10

I am writing a macro to run fixed effect regressions with clustering using the demeaning method as normal procedures give memory errors. With my current code, I have to modify the macro everytime I run a different regression as the variables are different and I have to get the means of them. I would like to write a macro which can apply to any variables I input without changing the macro. My current macro is:

%macro FEregression(dep,indep,clusterVar,FE_var); 
	      * To run with fixed effects use the method of subtracting off the mean for each date because the standard dummy variables approach needs too much memory;
	      proc sort data=panel; by &FE_var; run;
	      proc means data=panel print; by &FE_var; output out=means (drop=_TYPE_ _FREQ_) 
	            mean(&dep)=m&dep mean(A)=mA mean(B)=mB mean(C)=mC mean(D)=mD ; 
	      run;
	    	data means; merge panel means; by &FE_var; 
				&dep=&dep-m&dep;
				A=A-mA; B=B-mB;C=C-mC;D=D-mD;	
			run;
            proc surveyreg data=means; class &FE_var; cluster &clusterVar; * Cluster by clusterVar;
                  model &dep = &indep  / solution; 
            run; quit;
%mend;

%FEregression(Y, A B C D, , date)

So for this example, I am regressing Y on A B C D. 

1 ACCEPTED SOLUTION

Accepted Solutions
ballardw
Super User

So what you are asking is to replace in this code:

proc means data=panel print; 
  by &FE_var; 
  output out=means (drop=_TYPE_ _FREQ_) 
   mean(&dep)=m&dep mean(A)=mA mean(B)=mB mean(C)=mC mean(D)=mD ;  
run;

data means; 
merge panel means;
by &FE_var; &dep=&dep-m&dep; A=A-mA;
B=B-mB;
C=C-mC;
D=D-mD; run;

The A B C D variables as needed from the value of &indep where that is a list of variables?

 Or are A B C D a subset of the variables in &indep? If so, how do we know what the subset would be?

 

Things might get a lot simpler if you had a VAR statement on your Proc means like

Var &dep &indep;

and used the autoname option on out put instead of forcing use of the mA mB variables. mean(&dep &indep)= /autoname would append _mean to the name of each variable.

 

the data step could then become

data means; 
  merge panel means; 
  by &FE_var; 
  &dep=&dep- &dep._mean;
  %do i= 1 %to %sysfunc(countw(&indep));
   %let tvar= %scan(&indep,&i);
   &tvar = &tvar - &tvar._mean;
  %end;
 run;

View solution in original post

2 REPLIES 2
ballardw
Super User

So what you are asking is to replace in this code:

proc means data=panel print; 
  by &FE_var; 
  output out=means (drop=_TYPE_ _FREQ_) 
   mean(&dep)=m&dep mean(A)=mA mean(B)=mB mean(C)=mC mean(D)=mD ;  
run;

data means; 
merge panel means;
by &FE_var; &dep=&dep-m&dep; A=A-mA;
B=B-mB;
C=C-mC;
D=D-mD; run;

The A B C D variables as needed from the value of &indep where that is a list of variables?

 Or are A B C D a subset of the variables in &indep? If so, how do we know what the subset would be?

 

Things might get a lot simpler if you had a VAR statement on your Proc means like

Var &dep &indep;

and used the autoname option on out put instead of forcing use of the mA mB variables. mean(&dep &indep)= /autoname would append _mean to the name of each variable.

 

the data step could then become

data means; 
  merge panel means; 
  by &FE_var; 
  &dep=&dep- &dep._mean;
  %do i= 1 %to %sysfunc(countw(&indep));
   %let tvar= %scan(&indep,&i);
   &tvar = &tvar - &tvar._mean;
  %end;
 run;
somebody
Lapis Lazuli | Level 10

yes. that is correct, so that I dont have to change those steps everytime I run a different regression. So A B C D would be all the independent variables in the list &indep. Essentially, what I would like to do is to simply regress a new regression, say Z = M N O P Q by running %FEregression(Z, M N O P Q, clusterVar,date). To do this using my current method, I would have to rewrite the PROC MEANS and DATA step in my macro

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