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.;
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