Thanks for your advices ... View more

I'm sorry to bother you again. Can you help me with adding the weighting matrix to this, that is the inverse covariance matrix of the errors ? Also because when adding more moments condition the solutions diverge. proc model data=sasdata.mds_betas_portfolios_factors1 plot=none;     array bm[11] bm0-bm10;        array bs[11] bs0-bs10;        array bh[11] bh0-bh10;        array bf[11] bf0-bf10;        array mom[66] mom0-mom65;        parms bm0-bm10 bs0-bs10 bh0-bh10 bf0-bf10 labda;        do j = 0 to 10;       i = j+1;           mom = (p_mretrf - bf*(labda+0.026987772) - bm*mkt_rf                       - bs*smb - bh*hml - bf*mdi)*(j=preranking_factor_beta);         mom[i+11]= mkt_rf*(p_mretrf - bf*(labda+0.026987772) - bm*mkt_rf                       - bs*smb - bh*hml - bf*mdi)*(j=preranking_factor_beta);         mom[i+22]= smb*(p_mretrf - bf*(labda+0.026987772) - bm*mkt_rf                       - bs*smb - bh*hml - bf*mdi)*(j=preranking_factor_beta);         mom[i+33]= hml*(p_mretrf - bf*(labda+0.026987772) - bm*mkt_rf                       - bs*smb - bh*hml - bf*mdi)*(j=preranking_factor_beta);         mom[i+44]= mdi*(p_mretrf - bf*(labda+0.026987772) - bm*mkt_rf                       - bs*smb - bh*hml - bf*mdi)*(j=preranking_factor_beta);         mom[i+55]= (mdi-0.026987772)*(j=preranking_factor_beta);        end;        %geneqs(65);        fit resid0-resid65/gmm ;    quit; ... View more

Many thanks for your help! ... View more

Thanks for your advice! I have used the GMM version of my model program for both the OLS ans GMM methods. For the OLS method I get the same estimations as of the OLS version, but different standard errors ( the standard errors of the GMM version look a lot better). When using GMM method the answers is still different than that of the OLS, which I still don't understand. ... View more

Thank you very much for your help and your time. Now I have two proc models one for the OLS and one for GMM. But they both give me different estimators, which I did not expect. I thought that they would give exactly the same estimators, except for the standard errors. And can I also add the weight which is the inverse of the of the variance of the errors?   For the OLS: proc model data=sasdata.mds_betas_portfolios_factors1;      array bm[11] bm0-bm10;        array bs[11] bs0-bs10;        array bh[11] bh0-bh10;        array bf[11] bf0-bf10;        parms bm0-bm10 bs0-bs10 bh0-bh10 bf0-bf10 labda;        * time-series regression "by preranking_factor_beta" ;    mom1 = 0;          do i = 1 to 11;       mom1 = mom1 + (p_mretrf - bf*(labda+0.026987772)- bm*mkt_rf - bs*smb - bh*hml - bf*mdi)*(i= preranking_factor_beta+1);    end;    eq.resid1 = mom1;    fit resid1; quit; And for the GMM: proc model data=sasdata.mds_betas_portfolios_factors1 plot=none;    array bm[11] bm0-bm10;    array bs[11] bs0-bs10;    array bh[11] bh0-bh10;    array bf[11] bf0-bf10;    array mom[11] mom0-mom10;    parms bm0-bm10 bs0-bs10 bh0-bh10 bf0-bf10 labda;    do j = 0 to 10;       i = j+1;       mom = (p_mretrf - bf*(labda+0.026987772) - bm*mkt_rf                       - bs*smb - bh*hml - bf*mdi)*(j=preranking_factor_beta);    end;      %geneqs(10);      fit resid0-resid10 /  gmm; quit; ... View more

Thanks for your answer! After adjustments and lots of other trial and errors. It still gave me the error: ERROR: The number of parameters (371) is greater than the number of unique instruments (18) times the number of equations (2). and still the note: NOTE: The parameter bm0 is shared by all 2 of the equations to be estimated I also try it with this code below where I include labda in the formula already (without estimating 324 labda_t and then take their average, like in first attempt). But I still have more parameters than unique instruments. How can the ERROR and the NOTE be helped? proc model data=sasdata.mds_betas_portfolios_factors_rd;    array bm[11] bm0-bm10;    array bs[11] bs0-bs10;    array bh[11] bh0-bh10;    array bf[11] bf0-bf10;    parms bm0-bm10 bs0-bs10 bh0-bh10 bf0-bf10 labda;    * time-series regression "by preranking_factor_beta" ;    mom1 = 0;    do i = 0 to 10;       mom1 = mom1 + (p_mretrf - bf*(labda+0.026987772)- bm*mkt_rf - bs*smb - bh*hml - bf*mdi)*(i= preranking_factor_beta);    end;    eq.resid1 = mom1;       instruments mkt_rf smb hml mdi;    fit resid1/gmm; quit; ... View more

Many thanks for your help. I have modify the code a bit for my sample. But I think that I have done something wrong. The program just keeps running and did not give any results yet. And can I impose more moment conditions in the same way? I have add my data set above. proc model data=sasdata.mds_betas_portfolios_factors plot=none;    array bm[11] bm0-bm10;    array bs[11] bs0-bs10;    array bh[11] bh0-bh10;    array bf[11] bf0-bf10;    array labda[324] labda1-labda324;    parms bm0-bm10 bs0-bs10 bh0-bh10 bf0-bf10 labda1-labda324 lavg;      * time-series regression "by preranking_factor_beta" ;    tdim = 0;    do i = 0 to 10;       tdim = tdim + (p_mretrf - bf*(lavg+0.026987772)- bm*mkt_rf - bs*smb - bh*hml - bf*mdi)*(t=preranking_factor_beta);    end;    * cross-sectional regression "by caldt";    cdim = 0;    do i = caldt.first to caldt.last;   * this one is not working. I think that I have to make extra variable that run from 1-324 for the number of date? ;            do j = 0 to 10;               cdim = cdim + (p_mretrf - bm *mkt_rf - bs *smb - bh *hml - bf *labda)*(caldt=i);         end;    end;    eq.tresid = tdim;    eq.csresid = cdim;    restrict lavg = mean(labda1-labda324);   * I'm not sure that this one will work like this??;    fit tresid csresid / gmm; quit; .... in the end... I get the note e.g.: NOTE: The parameter bm4 is shared by all 2 of the equations to be estimated. ... View more

Hi Marc, Lots of thanks for your help. That is exactly my problems. I have to do a multivariate regression on cross-sectional data. - First step is running a time-series regressions; so different betas_i for different id. - Second step is running a cross-sectional regression at each time t, get λ t  and taking the average of the estimate; so λ = mean (λ_t). But I don't know how to do this. I appreciate it a lot if you can help me with a clarification of this technique for multivariate GMM ... View more

Thanks!. ... View more

this one just duplicated the id number 0 and 9. I would like to create new id 10 that has the different of 0 and 9 as value ... View more