(This is related to my (so far unanswered) last question)
I want to use residual bootstrap to examine uncertainty and robustness of a model that fits a series of environmental measurements. I know close to nothing about the structure of my data's noise. It appears to be heteroskedastic and may (!) be autocorrelated in some cases (I have a few different data sets). Would the most simple form of the wild bootstrap (switching signs of the residuals with 50 % probability) be valid in this context, or would I have to rely on harder to implement methods like (moving) block bootstrapping because of possible autocorrelation?
Please stop me if I'm completely off the track; while the idea of bootstrapping is more or less easy to understand, the many available variants are extremely confusing to a newcomer.
/edit: Just to be clear: I want to resample the residuals (as an estimate for the error), not the time-series itself.