Random numbers seed setting

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Rabeya
Rabeya 2013-4-19
Hi,
I am using randn('seed', 1) at the beginning of my code for a simulation with 1000 replications. This is to make sure that if I rerun the simulation I'll get same results every time. But I am getting slightly different results in different runs. Can anyone help? I am using Matlab 2012a.

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Azzi Abdelmalek
Azzi Abdelmalek 2013-4-19
Use
a=rng;
out=randn
rng(a)
out=randn
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Rabeya
Rabeya 2013-4-19
编辑：Azzi Abdelmalek 2013-4-19
clear
clc
randn('seed',1);
rep =1000; % number of replications
k=2; % number of parameters
onesr = ones(rep,1);
beta_true=[0;0.05];
% for data:
obsvec=100;%[5;10;50;100];%;1000];
obsval=length(obsvec);
expand=26; % each cohort observed over 26 years
Cvec=30;%[27;30;40;60];%[60;12;6]; % no of cohorts
Gvec=15;%[3;10;30];%[3;3;4;6];%[4;10;40]; %[6;2]; % no of groups
Cval=length(Cvec);
Gval = length(Gvec);
ci = 0;
while ci < Cval %takes different number of cohorts
ci = ci+1;
C = Cvec(ci);
%G = Gvec(ti);
oi=0;
while oi<obsval
oi=oi+1;
obs=obsvec(oi);
gi=0;
while gi < Gval %takes different number of groups
gi=gi+1;
G=Gvec(gi);
% storage for the beta-estimator values
beta2sls=zeros(rep,k);
se_beta2sls=zeros(rep,k);
betaGMM=zeros(rep,k);
se_betaGMM=zeros(rep,k);
J_stat_GMM=zeros(rep,1);
pval_GMM=zeros(rep,1);
beta0=zeros(rep,1);
betaEL=zeros(rep,k);
se_betaEL=zeros(rep,k);
bias_crrctdEL=zeros(rep,k);
se_bias_crrctdEL=zeros(rep,k);
lik_EL=zeros(rep,1);
J_statEL=zeros(rep,1);
pvalEL=zeros(rep,1);
betaET=zeros(rep,k);
se_betaET=zeros(rep,k);
bias_crrctdET=zeros(rep,k);
se_bias_crrctdET=zeros(rep,k);
lik_ET=zeros(rep,1);
J_statET=zeros(rep,1);
pvalET=zeros(rep,1);
c_GMM=zeros(rep,1);
c_EL=zeros(rep,1);
c_ET=zeros(rep,1);
wald_EL=zeros(rep,1);
pval_wald_EL=zeros(rep,1);
c_wald_EL=zeros(rep,1);
LM_EL=zeros(rep,1);
pval_LM_EL=zeros(rep,1);
c_LM_EL=zeros(rep,1);
wald_ET=zeros(rep,1);
pval_wald_ET=zeros(rep,1);
c_wald_ET=zeros(rep,1);
LM_ET=zeros(rep,1);
pval_LM_ET=zeros(rep,1);
c_LM_ET=zeros(rep,1);
bar_ug=zeros(G,rep);
sigma2_ug=zeros(G,rep);
ug_3=zeros(G,rep);
xu_g=zeros(G,k-1,rep);
N=zeros(rep,1);
se=[2;1.8;1.75;1.71;1.6;1.5;1.45;1.41;1.4;1.36;1.2;1.11;1.05;1.05;1.02;1;0.99;0.95;0.91;0.9;0.86;0.88;0.84;0.8;0.84;0.7;0.66;0.5;0.43;0.39];
r = 0;
while r < rep % replication
r = r+1;
data=Data_RCSabilhet_step(C,obs,expand,se);
%data=pseudo2_data(T,obs,expand); % calling the data
[x,y,u,group,ng]=xyu_RCSabilhet_G15_withcons(data); % forming the variables
N(r,1)=sum(ng);
[bar_ug(:,r), sigma2_ug(:,r), ug_3(:,r),xu_g(:,:,r)]=moment_u(x,u,ng,group);
z=dummyvar(group); % group dummies, needed for GMM estimation
% GMM estimation
[beta2sls(r,:),se_beta2sls(r,:), iter, betaGMM(r,:),se_betaGMM(r,:), J_stat_GMM(r,:), pval_GMM(r,:)]=igmm_grouped_data(y,x,z,ng);
if pval_GMM(r,:)>=0.05 % at 5% sig level
c_GMM(r,:)=1; % number of rejections
end
% EL estimators:
[betaEL(r,:),lik_EL(r,:),lambda,p]=EL_optim_owen(x,y,G,ng,betaGMM(r,:)');
se_betaEL(r,:)=diag(se_beta_GEL(betaEL(r,:)',x,y,group,ng,p));
bias_EL=bias_GEL(x,y,G,ng,betaEL(r,:)',-2,p);
bias_crrctdEL(r,:)=betaEL(r,:)-bias_EL';
se_bias_crrctdEL(r,:)=diag(se_beta_GEL(bias_crrctdEL(r,:)',x,y,group,ng,p));
J_statEL(r,:)=2*lik_EL(r,:); % overidentification test stat
pvalEL(r,:)=1-chi2cdf(J_statEL(r,:),G-k); % pvalue of the test statistic
if pvalEL(r,:)>=0.05 % at 5% sig level
c_EL(r,:)=1; % number of rejections
end
wald_EL(r,:)=wald_test(x,y,ng,G,betaEL(r,:)',p);
pval_wald_EL(r,:)=1-chi2cdf(wald_EL(r,:),G-k);
if pval_wald_EL(r,:)>=0.05 % at 5% sig level
c_wald_EL(r,:)=1; % number of rejections
end
LM_EL(r,:)=LM_test(x,y,ng,G,betaEL(r,:)',lambda,p);
pval_LM_EL(r,:)=1-chi2cdf(LM_EL(r,:),G-k);
if pval_LM_EL(r,:)>=0.05 % at 5% sig level
c_LM_EL(r,:)=1; % number of rejections
end
% ET estimators:
[betaET(r,:),lik_ET(r,:),lambda,p_ET]=ET_optim(x,y,G,ng,betaGMM(r,:)');
se_betaET(r,:)=diag(se_beta_GEL(betaET(r,:)',x,y,group,ng,p_ET));
bias_ET=bias_GEL(x,y,G,ng,betaET(r,:)',-1,p_ET);
bias_crrctdET(r,:)=betaET(r,:)-bias_ET';
se_bias_crrctdET(r,:)=diag(se_beta_GEL(bias_crrctdET(r,:)',x,y,group,ng,p_ET));
J_statET(r,:)=(2*N(r,1)+2*lik_ET(r,:)); % overidentification test stat
pvalET(r,:)=1-chi2cdf(J_statET(r,:),G-k); % pvalue of the test statistic
if pvalET(r,:)>=0.05 % at 5% sig level
c_ET(r,:)=1; % number of rejections
end
wald_ET(r,:)=wald_test(x,y,ng,G,betaET(r,:)',p_ET);
pval_wald_ET(r,:)=1-chi2cdf(wald_ET(r,:),G-k);
if pval_wald_ET(r,:)>=0.05 % at 5% sig level
c_wald_ET(r,:)=1; % number of rejections
end
LM_ET(r,:)=LM_test(x,y,ng,G,betaET(r,:)',lambda,p_ET);
pval_LM_ET(r,:)=1-chi2cdf(LM_ET(r,:),G-k);
if pval_LM_ET(r,:)>=0.05 % at 5% sig level
c_LM_ET(r,:)=1; % number of rejections
end
end %end of rep
beta1='educ';
b_names=char(strvcat(beta1));
medbias2sls = median(beta2sls-onesr*beta_true');
medbiasGMM = median(betaGMM-onesr*beta_true');
medbiasEL = median(betaEL-onesr*beta_true');
medbiasEL_biascorr = median(bias_crrctdEL-onesr*beta_true');
medbiasET = median(betaET-onesr*beta_true');
medbiasET_biascorr = median(bias_crrctdET-onesr*beta_true');
covrateGMM=ones(1,k)-(onesr'*(abs((betaGMM-onesr*beta_true')./se_betaGMM)>1.6449*onesr*ones(1,k)))/rep;
covrate2sls=ones(1,k)-(onesr'*(abs((beta2sls-onesr*beta_true')./se_beta2sls)>1.6449*onesr*ones(1,k)))/rep;
covrateEL=ones(1,k)-(onesr'*(abs((betaEL-onesr*beta_true')./se_betaEL)>1.6449*onesr*ones(1,k)))/rep;
covrateEL_biascorr=ones(1,k)-(onesr'*(abs((bias_crrctdEL-onesr*beta_true')./se_bias_crrctdEL)>1.6449*onesr*ones(1,k)))/rep;
covrateET=ones(1,k)-(onesr'*(abs((betaET-onesr*beta_true')./se_betaET)>1.6449*onesr*ones(1,k)))/rep;
covrateET_biascorr=ones(1,k)-(onesr'*(abs((bias_crrctdET-onesr*beta_true')./se_bias_crrctdET)>1.6449*onesr*ones(1,k)))/rep;
fprintf('nrep = % 4.0f \n', rep );
fprintf('Cohorts = % 4.0f \n', C );
fprintf('obs = % 4.0f \n', obs );
fprintf('G = % 4.0f \n', G );
fprintf('N = % 4.0f \n', mean(N));
fprintf('k = % 4.0f \n', k );
fprintf('beta = % s \n', b_names);
fprintf('\n');
fprintf('moments of u \n');
fprintf(' mean variance third moment \n');
for jj=1:G
fprintf('% 4.6f % 4.6f % 4.6f \n',mean(bar_ug(jj,:)),mean(sigma2_ug(jj,:)),mean(ug_3(jj,:)));
end
fprintf('\n');
fprintf('Correlation of x and u \n');
for jj=1:G
for ii=1:k-1
fprintf('% 4.6f \n',mean(xu_g(jj,ii,:)));
end
end
fprintf('\n');
fprintf('Estimators Mean Estimate Mean se Median Estimate Median se \n');
for jj=1:k
fprintf('2SLS % 4.8f % 4.8f % 4.8f % 4.8f\n', mean(beta2sls(:,jj)), mean(se_beta2sls(:,jj)), median(beta2sls(:,jj)), median(se_beta2sls(:,jj)));
fprintf('GMM % 4.8f % 4.8f % 4.8f % 4.8f\n', mean(betaGMM(:,jj)), mean(se_betaGMM(:,jj)),median(betaGMM(:,jj)), median(se_betaGMM(:,jj)));
fprintf('EL % 4.8f % 4.8f % 4.8f % 4.8f\n', mean(betaEL(:,jj)), mean(se_betaEL(:,jj)),median(betaEL(:,jj)), median(se_betaEL(:,jj)));
fprintf('EL_biascorr % 4.8f % 4.8f % 4.8f % 4.8f\n', mean(bias_crrctdEL(:,jj)), mean(se_bias_crrctdEL(:,jj)), median(bias_crrctdEL(:,jj)), median(se_bias_crrctdEL(:,jj)));
fprintf('ET % 4.8f % 4.8f % 4.8f % 4.8f\n', mean(betaET(:,jj)), mean(se_betaET(:,jj)),median(betaET(:,jj)), median(se_betaET(:,jj)));
fprintf('ET_biascorr % 4.8f % 4.8f % 4.8f % 4.8f\n', mean(bias_crrctdET(:,jj)), mean(se_bias_crrctdET(:,jj)), median(bias_crrctdET(:,jj)), median(se_bias_crrctdET(:,jj)));
end
fprintf('\n');
fprintf('Estimators Median Bias Coverage rate \n');
for jj=1:k
fprintf('2SLS % 4.8f % 4.4f\n', medbias2sls(:,jj), covrate2sls(:,jj));
fprintf('GMM % 4.8f % 4.4f\n', medbiasGMM(:,jj), covrateGMM(:,jj));
fprintf('EL % 4.8f % 4.4f\n', medbiasEL(:,jj), covrateEL(:,jj));
fprintf('EL_biascorr % 4.8f % 4.4f\n', medbiasEL_biascorr(:,jj), covrateEL_biascorr(:,jj));
fprintf('ET % 4.8f % 4.4f\n', medbiasET(:,jj), covrateET(:,jj));
fprintf('ET_biascorr % 4.8f % 4.4f\n', medbiasET_biascorr(:,jj), covrateET_biascorr(:,jj));
end
fprintf('\n');
fprintf('Number of acceptances of GMM J test(5 percent sig level) % 4.0f \n', sum(c_GMM));
fprintf('EL \n');
fprintf('EL \n');
fprintf('Number of acceptances of overid test(5 percent sig level) % 4.0f \n', sum(c_EL));
fprintf('Number of acceptances of wald test(5 percent sig level) % 4.0f \n', sum(c_wald_EL));
fprintf('Number of acceptances of LM test(5 percent sig level) % 4.0f \n', sum(c_LM_EL));
fprintf('\n');
fprintf('ET \n');
fprintf('Number of acceptances of overid test(5 percent sig level) % 4.0f \n', sum(c_ET));
fprintf('Number of acceptances of wald test(5 percent sig level) % 4.0f \n', sum(c_wald_ET));
fprintf('Number of acceptances of LM test(5 percent sig level) % 4.0f \n', sum(c_LM_ET));
fprintf('\n');
end % end of different groups
end % end of different obs
end % end of different cohorts
Digitalsd
Digitalsd 2013-4-19
In addition you can use the code:
stream = RandStream('mt19937ar','seed',1);
RandStream.setGlobalStream(stream);
randn
stream = RandStream('mt19937ar','seed',1);
RandStream.setGlobalStream(stream);
randn
"mt19937ar" indicates the used p.s.random generator algorithm

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