I was trying to disaggregate my monthly dataset to daily dataset using an indicator variable. I checked online and realized that there is an R package called tempdisagg available here


However, when I was trying to read this code and see the pros and cons of the available methods used as discussed in this paper. https://mpra.ub.uni-muenchen.de/53389/1/sax-steiner.pdf

I realized that "all disaggregation methods ensure that either the sum, the average, the first or the last value of the resulting high frequency series is consistent with the low frequency series. They can deal with situations where the high frequency is an integer multiple of the low frequency" .... ", but not with irregular frequencies," e.g. month to weeks.

The authors are referring to the temporal disaggregation methods of: Denton, Denton-Cholette, Chow-Lin, Fernandez and Litterman.

This is also mentioned in the abstract of the following page https://journal.r-project.org/archive/2013/RJ-2013-028/index.html

My question is: Is this right based on a theoretical understanding of their methods or is it based on a programming coding issue.

So if there is nothing wrong with the theory in irregular frequencies is that a theoretical drawback of all these methods?


1 Answer 1


tempdisagg 1.0, which is now on CRAN, can convert to irregular frequencies. See the following blog post for how to disaggregate a quarterly series to daily:



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