http://www.stata.com/statalist/archive/2013-08/msg00555.html (and nexts in threads, of course) ]]>

it would not be easy. ]]>

So how useful is an asymptotic result in a given real situation if its validity depends on how the reader believes the statistician would choose b with much larger n, which is a choice that in fact the statistician never has to make? ]]>

You need to divide the data into blocks and subsample blocks.

Most of the Romano book is actually about the dependent case.

If you google “time series subsampling” you’ll

find lots of good stuff. ]]>

I also wonder whether this is could be used from regression discontinuity estimates, the literature seems to be heading into a different direction, e.g. http://www-personal.umich.edu/~cattaneo/papers/RD-biascorrection.pdf

And by the way, if we are “sticking to the data” this much, are we getting standard errors (closer to those) conditional on covariates, or not?

I basically mean this paper: http://www.nber.org/papers/w17442.pdf