Although it may only be of little help, the problem you present to me is synonymous with the "Change of Support" problem encountered when using areal units. Although this work just presents a framework for what you describe as "reglarize and interpolate" using a method referred to as "kriging". I don't think any of this work will help answer your question of whether estimating your missing values in the series in such a manner will bias error correction estimates, although if some of your samples are in clustered time intervals for both series you may be able to check for yourself. You may also be interested in the technique of "co-kriging" from this field, which uses information from one source to estimate the value for another (if your interested I would suggest you check out the work being done by Pierre Goovaerts).
Again I'm not sure how helpful this will be though. It may be substantially simpler to just use current time-series forecasting techniques to estimate your missing data. It won't help you decide what to estimate either.
Good luck, and keep the thread updated if you find any pertinent material. I would be interested, and you would think with the proliferation of data sources online this would become an pertinent issue for at least some research projects.