how to find pairwise residual correlations after 2sls estimation using proc model in sas?

[linaad]

i'm using 2sls to estimate my model which contains 7 dependent variables: Y1,Y2,Y3,Y4,Y5,Y6,Y7.  There is no problem to run the code, however, i want to save the residuals from these estimations, and i want to find the pairwise residual correlations of these residuals and print them out.  how can i code this into the proc model ?

here is my code:

Proc model data=main;
Y1=a0+a1*lag(Y1)+
a5*(W1_2*Y2 + W1_3*Y3 + W1_4*Y4 + W1_5*Y5 + W1_6*Y6 +W1_7*Y7)+
a6*lag(W1_2*Y2 + W1_3*Y3 + W1_4*Y4 + W1_5*Y5 + W1_6*Y6 +W1_7*Y7)+
a7*lag2(W1_2*Y2 + W1_3*Y3 + W1_4*Y4 + W1_5*Y5 + W1_6*Y6 +W1_7*Y7)+
a8*lag3(W1_2*Y2 + W1_3*Y3 + W1_4*Y4 + W1_5*Y5 + W1_6*Y6 +W1_7*Y7) +
a9*lag4(W1_2*Y2 + W1_3*Y3 + W1_4*Y4 + W1_5*Y5 + W1_6*Y6 +W1_7*Y7);
Y2=b0+b1*lag(Y2)+
b5*(W2_1*Y1 + W2_3*Y3 + W2_4*Y4 + W2_5*Y5 + W2_6*Y6 + W2_7*Y7)+
b6*lag(W2_1*Y1 + W2_3*Y3 + W2_4*Y4 + W2_5*Y5 + W2_6*Y6 + W2_7*Y7)+
b7*lag2(W2_1*Y1 + W2_3*Y3 + W2_4*Y4 + W2_5*Y5 + W2_6*Y6 + W2_7*Y7)+
b8*lag3(W2_1*Y1 + W2_3*Y3 + W2_4*Y4 + W2_5*Y5 + W2_6*Y6 + W2_7*Y7)+
b9*lag4(W2_1*Y1 + W2_3*Y3 + W2_4*Y4 + W2_5*Y5 + W2_6*Y6 + W2_7*Y7);
Y3=c0+c1*lag(Y3)+
c5*(W3_1*Y1 + W3_2*Y2 + W3_4*Y4 + W3_5*Y5 + W3_6*Y6 + W3_7*Y7)+
c6*lag(W3_1*Y1 + W3_2*Y2 + W3_4*Y4 + W3_5*Y5 + W3_6*Y6 + W3_7*Y7)+
c7*lag2(W3_1*Y1 + W3_2*Y2 + W3_4*Y4 + W3_5*Y5 + W3_6*Y6 + W3_7*Y7)+
c8*lag3(W3_1*Y1 + W3_2*Y2 + W3_4*Y4 + W3_5*Y5 + W3_6*Y6 + W3_7*Y7)+
c9*lag4(W3_1*Y1 + W3_2*Y2 + W3_4*Y4 + W3_5*Y5 + W3_6*Y6 + W3_7*Y7);
Y4=d0+d1*lag(Y4)+
d5*(W4_1*Y1 + W4_2*Y2 + W4_3*Y3 + W4_5*Y5 + W4_6*Y6 + W4_7*Y7)+
d6*lag(W4_1*Y1 + W4_2*Y2 + W4_3*Y3 + W4_5*Y5 + W4_6*Y6 + W4_7*Y7)+
d7*lag2(W4_1*Y1 + W4_2*Y2 + W4_3*Y3 + W4_5*Y5 + W4_6*Y6 + W4_7*Y7)+
d8*lag3(W4_1*Y1 + W4_2*Y2 + W4_3*Y3 + W4_5*Y5 + W4_6*Y6 + W4_7*Y7)+
d9*lag4(W4_1*Y1 + W4_2*Y2 + W4_3*Y3 + W4_5*Y5 + W4_6*Y6 + W4_7*Y7);
Y5=e0+e1*lag(Y5)+
e5*(W5_1*Y1 + W5_2*Y2 + W5_3*Y3 + W5_4*Y4 + W5_6*Y6 + W5_7*Y7)+
e6*lag(W5_1*Y1 + W5_2*Y2 + W5_3*Y3 + W5_4*Y4 + W5_6*Y6 + W5_7*Y7)+
e7*lag2(W5_1*Y1 + W5_2*Y2 + W5_3*Y3 + W5_4*Y4 + W5_6*Y6 + W5_7*Y7)+
e8*lag3(W5_1*Y1 + W5_2*Y2 + W5_3*Y3 + W5_4*Y4 + W5_6*Y6 + W5_7*Y7)+
e9*lag4(W5_1*Y1 + W5_2*Y2 + W5_3*Y3 + W5_4*Y4 + W5_6*Y6 + W5_7*Y7);
Y6=f0+f1*lag(Y6)+
f5*(W6_1*Y1 + W6_2*Y2 + W6_3*Y3 + W6_4*Y4 + W6_5*Y5 + W6_7*Y7)+
f6*lag(W6_1*Y1 + W6_2*Y2 + W6_3*Y3 + W6_4*Y4 + W6_5*Y5 + W6_7*Y7)+
f7*lag2(W6_1*Y1 + W6_2*Y2 + W6_3*Y3 + W6_4*Y4 + W6_5*Y5 + W6_7*Y7)+
f8*lag3(W6_1*Y1 + W6_2*Y2 + W6_3*Y3 + W6_4*Y4 + W6_5*Y5 + W6_7*Y7)+
f9*lag4(W6_1*Y1 + W6_2*Y2 + W6_3*Y3 + W6_4*Y4 + W6_5*Y5 + W6_7*Y7);
Y7=g0+g1*lag(Y7)+
g5*(W7_1*Y1 + W7_2*Y2 + W7_3*Y3 + W7_4*Y4 + W7_5*Y5 + W7_6*Y6)+
g6*lag(W7_1*Y1 + W7_2*Y2 + W7_3*Y3 + W7_4*Y4 + W7_5*Y5 + W7_6*Y6)+
g7*lag2(W7_1*Y1 + W7_2*Y2 + W7_3*Y3 + W7_4*Y4 + W7_5*Y5 + W7_6*Y6)+
g8*lag3(W7_1*Y1 + W7_2*Y2 + W7_3*Y3 + W7_4*Y4 + W7_5*Y5 + W7_6*Y6)+
g9*lag4(W7_1*Y1 + W7_2*Y2 + W7_3*Y3 + W7_4*Y4 + W7_5*Y5 + W7_6*Y6);
endogenous Y1-Y7;
exogenous W1_2-W1_7 W2_1 W2_3-W2_7 W3_1 W3_2 W3_4-W3_7 W4_1-W4_3 W4_5-W4_7
W5_1-W5_4 W5_6-W5_7 W6_1-W6_5 W6_7 W7_1-W7_6;
instruments y1_1-y1_4 y2_1-y2_4 y3_1-y3_4 y4_1-y4_4 y5_1-y5_4 y6_1-y6_4
y7_1-y7_4;
parms a0 a1 a5-a9 b0 b1 b5-b9 c0 c1 c5-c9 d0 d1 d5-d9 e0 e1 e5-e9 f0 f1 f5-f9 g0 g1 g5-g9;
range nyear= 1996.1 to 2017.4;
fit Y1-Y7/2sls outest=eat1.est1;
run;

[mkeintz]

Have you looked at the FIT statement documentation?  I see an "outresid=" parameter that makes a dataset of residuals, which you can then submit to proc corr.