Hello everyone,
I'm a beginner in sas and I'm wondering a question.
I have 10 models to estimate in order to say which is the correct one to predict returns (rp). Consequently, I decided to make 12 main comparisons (2 by 2) of my regression models by statistically comparing each time only my 2 intercepts (not the other betas coefficients) with tests. The trouble is I do not want to do this only with one returns serie but ultimately with around 2,000 (returns series). However, for now, I would like to make it as easy as I can. If I can do this on 1 serie, I can do it on 2,000.
So ultimately, I would like firstly to save on a distinct database all the parameters of my 10 regressions models (intercepts, bêtas coefficients, p-value, F-test, etc.). Secondly, I would like, for each model,to compute intercepts mean, intercepts percentiles, intercepts distribution, etc. Secondly, to know which model is correct to predict returns, I would like to compare 2 by 2 all of my 10 models by testing each time whether the 2 intercepts are statistically the same or not.
Please find below my 12 comparisons:
proc reg data=modelization outest=est; /*1*/
M1: model rp=rm / selection=rsquare b best=1;
MFS1: model rp=rm rm_zdy rm_ztbl rm_ztms rm_zdfy / selection=rsquare b best=1;
proc print data=est;
run;
proc reg data=modelization outest=est; /*2*/
M2: model rp=rm rmrsq / selection=rsquare b best=1;
MFS2: model rp=rm rm_zdy rm_ztbl rm_ztms rm_zdfy rmrsq / selection=rsquare b best=1;
proc print data=est;
run;
proc reg data=modelization outest=est; /*3*/
M1: model rp=rm / selection=rsquare b best=1;
MPM10: model rp=rm rm_pred_mean / selection=rsquare b best=1;
proc print data=est;
run;
proc reg data=modelization outest=est; /*4*/
M2: model rp=rm rmrsq / selection=rsquare b best=1;
MPM11: model rp=rm rm_pred_mean rmrsq / selection=rsquare b best=1;
proc print data=est;
run;
proc reg data=modelization outest=est; /*5*/
M1: model rp=rm / selection=rsquare b best=1;
MCFG1: model rp=rm zdy ztbl ztms zdfy rm_zdy rm_ztbl rm_ztms rm_zdfy / selection=rsquare b best=1;
proc print data=est;
run;
proc reg data=modelization outest=est; /*6*/
M2: model rp=rm rmrsq / selection=rsquare b best=1;
MCFG2: model rp=rm zdy ztbl ztms zdfy rm_zdy rm_ztbl rm_ztms rm_zdfy rmrsq / selection=rsquare b best=1;
proc print data=est;
run;
proc reg data=modelization outest=est; /*7*/
M1: model rp=rm / selection=rsquare b best=1;
MPM20: model rp=pred_mean rm rm_pred_mean / selection=rsquare b best=1;
proc print data=est;
run;
proc reg data=modelization outest=est; /*8*/
M2: model rp=rm rmrsq / selection=rsquare b best=1;
MPM21: model rp=pred_mean rm rm_pred_mean rmrsq / selection=rsquare b best=1;
proc print data=est;
run;
proc reg data=modelization outest=est; /*9*/
MFS1: model rp=rm rm_zdy rm_ztbl rm_ztms rm_zdfy / selection=rsquare b best=1;
MPM10: model rp=rm rm_pred_mean / selection=rsquare b best=1;
proc print data=est;
run;
proc reg data=modelization outest=est; /*10*/
MFS2: model rp=rm rm_zdy rm_ztbl rm_ztms rm_zdfy rmrsq / selection=rsquare b best=1;
MPM11: model rp=rm rm_pred_mean rmrsq / selection=rsquare b best=1;
proc print data=est;
run;
proc reg data=modelization outest=est; /*11*/
MCFG1: model rp=rm zdy ztbl ztms zdfy rm_zdy rm_ztbl rm_ztms rm_zdfy / selection=rsquare b best=1;
MPM20: model rp=pred_mean rm rm_pred_mean / selection=rsquare b best=1;
proc print data=est;
run;
proc reg data=modelization outest=est; /*12*/
MCFG2: model rp=rm zdy ztbl ztms zdfy rm_zdy rm_ztbl rm_ztms rm_zdfy rmrsq / selection=rsquare b best=1;
MPM21: model rp=pred_mean rm rm_pred_mean rmrsq / selection=rsquare b best=1;
proc print data=est;
run;
I hope that you can help me to write the codes relative to my goals.
Thank you,
