On Sun, 24 Oct 2010, Sergei B wrote: No, your question is not that obvious, and in fact you seem to have exposed a bug. If all the daily values for a given month are NA, then clearly the monthly summation should also be NA. This bug will be fixed shortly. In the meantime there are various work-arounds. One possibility is to use gretl's zeromiss() function, which turns zeros into missing values, on the compacted series: x = zeromiss(x) But obviously you'd want to do that only if you're confident that a monthly value of 0 is never a valid value. An alternative is to convert the NAs in the original daily data into some clearly out-of-bounds value, on the basis of which you can then correct the monthly series. Here is a complete script that illustrates what I'm talking about: # construct an artificial 3-month daily series nulldata 91 setobs 7 2010/09/01 # 7-day daily data series x = normal() # simulate your situation by making all the # October values into NAs series oct = (t > 30 && t < 62) x = oct ? NA : x # now make the NAs into (for example) -9999 x = ok(x)? x : -9999 # compact the data to monthly by summation dataset compact 12 sum # finally, convert "big negative numbers" to NA x = (x < -9000)? NA : x # and inspect the results print -o If some months contain just a few NAs at the daily level while others contain nothing but NAs, then you'd have to modify the above approach slightly, but hopefully you get the general idea. Allin Cottrell