Basically I need to ensure for all rows in the variable t1, that they are resampled in the same way.
That means that if the first draw is observation 2, then for all t1, I need observation 2 to be the first draw. This continues, such that I will have b resamples that resamples so that the samples are reordered, but for all t1 they has to be re-ordered the same way. By using rng I achieve this. I also need the variable "carhartt" to follow the same procedure.
However, I am questioning whether there is a better method for doing this? It is very important that i resample all t1 in the same way.
It is also important that each bootstrap/datasample is random.
Do any of you experts have a better solution, or do you approve of the one I use here?
I am trying to bootstrap/resample the best appropriate way:
y=+aDanmark()-bDanmark(:,5);
t1=y(1:t,:);
nans = any(isnan(t1),1);
t1(:,nans)=[];
for i =1:size(t1,2)
resultst1=ols(t1(1:12,i),carhart(1:12,:));
estimatest1(:,i)=resultst1.beta(1);
residuals=resultst1.resid;
carhartt=carhart(1:12,:);
b=99;
for B=1:b
rng(B);
bootresiduals=datasample(residuals,size(residuals,1));
rng(B);
bootcarhart=datasample(carhartt,size(residuals,1));
B
end
end
