I am trying to model an Almost Ideal Demand System on dynamic panel data SAS/ETS Examples -- Estimating an Almost Ideal Demand System Model
I am using a data set that consists of observations over 20 years and 18 cohorts (age groups). I need to use a GMM method since it is a dynamic equation. However, I have to model two regressions at once, and I am really quite not sure how I do that with proc panel? Do I repeat the model statement twice? Any help will be greatly appreciated! Here come the eqautions and the RESTRICTIONS.
Since I have 18 cohorts, I would like to regress this for EACH cohort (each group age). Do I use the BY statement for that purpose?
mean_w_alco = a10 + g11*log(norm_p_alco/norm_p_other) + g12*log(norm_p_toba/norm_p_other) + g1*log(norm_p_other) + b1*log(exp_tot/Laspeyres) + c1*z
mean_w_toba = a20 + g21*log(norm_p_alco/norm_p_other) + g22*log(norm_p_toba/norm_p_other) + g2*log(norm_p_other) + b2*log(exp_tot/Laspeyres) + c2*z
restrictions:
g12=-g11;
g21=-g22;
g12=g21;
b2=-b1;
c2=-c1;
a20=1-a10;
Would I do :
proc panel data=equa1;
by age;
model
mean_w_alco = a10 + g11*log(norm_p_alco/norm_p_other) + g12*log(norm_p_toba/norm_p_other) + g1*log(norm_p_other) + b1*log(exp_tot/Laspeyres) + c1*z+e+E1
mean_w_toba = a20 + g21*log(norm_p_alco/norm_p_other) + g22*log(norm_p_toba/norm_p_other) + g2*log(norm_p_other) + b2*log(exp_tot/Laspeyres) + c2*z+e+E2;
g12=-g11;
g21=-g22;
g12=g21;
b2=-b1;
c2=-c1;
a20=1-a10;
solve g12 g11 g21 g22 b2 b1 c2 c1 a20 a10;
run;
quit;
? It doesn't seem to work when I run this on SAS.;
Hello -
Sharing feedback from one of my colleagues in SAS R&D who works on the new SSM procedure, which you may find useful.
Note that SSM is experimental with 12.1: http://support.sas.com/documentation/cdl/en/etsug/63939/HTML/default/viewer.htm#ssm_toc.htm
Thanks,
Udo
The user has two response variables, say Y1 and Y2 and response variables X1-X4 and Z. The models are:
Y1 = a1 + g11 X1 + g12 X2 + g1 X3 + b1 X4 + c1 Z + e1
Y2 = (1-a1) + g21 X1 + g22 X2 + g2 X3 + b2 X4 + c2 Z + e2
Where
1. g11 = -g12 and g21 = -g22
2. G12 = g21
3. B2 = -b1
4. C2 = -c1
These restrictions can be imposed by redefining some variables:
The first restriction can be achieved by using (X1 – X2) as a single variable in place of X1 and X2. Let us call this its coefficient as G. Now the second restriction says that the coefficient G is to be the same for the two models (Y1 and Y2).
This can be achieved in SSM by treating the regression coefficient as a state element. See the section “Regression Variable Specification in Multivariate Models” in the SSM doc (Details->Overview of Model Specification Syntax).
Similar things can be done for a,B and C parameters. The user’s models appear simple reg models. He can specify more general error structures to account for time variation if that is desired.
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