It sounds like you should sample x and y together using the block bootstrap. If you have the usual situation, x and y in columns and observations in rows, then sample blocks of rows.
Even though observations in y are independent, you would take advantage of that only for bootstrapping statistics that depend only on y. The answer to your second question is the same as the first - sample blocks of observations, keeping x and y together. Tim Hesterberg >Hello. > >I have got two problems in bootstrapping from >dependent data sets. > >Given two time-series x and y. Both consisting of n >observations with x consisting of dependent and y >consisting of independent observations over time. Also >assume, that the optimal block-length l is given. > >To obtain my bootstrap sample, I have to draw >pairwise, but there is the problem of dependence of >the x-observations and so if I draw the third >observation of y, I cannot simply draw the third >observation of x (to retain the serial correlation >structure between x and y), because I devided x into >blocks of length l and I have to draw blocks, then I >draw from x. > >1. >How can I compute a bootstrap sample of the >correlation coefficient between x and y with respect >to the dependence in time-series of x? > >2. >How does it look like, if x and y both consist of >dependent observations? > > > >I hope you can help me. I got really stuck with this >problem. > >Sincerly >Klein. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
