# Hypothesis testing: small timeseries changes

I have pairs of timeseries that are estimating the same quantity over the years. It is some survival data: number of "dead" subjects during the year over the number of total subjects at the beginning of the year. The underlying data is regularly revised, so I need to assess the effect of the changes. Below an example is given.

I want to specifically test if the mean (which is the parameter of interest) is the same for the timeseries pairs (the analysis will be repeated for a lot of pairs, please do not focus too much on the particular image although it is fairly representative).

I have several questions:

1. Should I use a paired test or not?
2. If it is paired, how do I deal with the ties, i.e. zero differences (I will always have a lot of ties)?
3. I believe that bootstrapping can be usefull in this case. I have bootstrapped the sample mean difference. This gives a distribution centered around the observed mean difference. How can I compute a p-value out of this distribution? Would it be reasonable to bootstrap the t-test instead?