Hi, it seems to me that movavg() (and perhaps other "intertemporal" functions as well?) could/should also handle missing values in the middle of time series, and not only like they currently seem to be doing at the start or end. Consider this example, where a manual moving average calculation works just fine (of course producing NAs where required data are missing, but that's ok), but the built-in movavg() function refuses to do anything: <hansl> nulldata 10 setobs 1 1 --time-series series in = NA # some missings, but... smpl 6 10 # ... a sub-sample... series in = index # ...with valid values smpl --full # compare built-in and manual mov. avg. series outma = movavg(in, 4) series outclone = (in + in(-1) + in(-2) + in(-3)) / 4 d = sum(abs(outma - outclone)) # ok, same results # now set 1st obs as non-missing, too in[1] = 0 # again (try to) compare series outclone2 = (in + in(-1) + in(-2) + in(-3)) / 4 catch series outma2 = movavg(in, 4) # missing value error if $error print outclone2 # ok with some missings (of course) endif </hansl> This is with yesterday's snapshot (on Win 8). If you're wondering why time series would have gaps in the middle, well this happens with real-world data for example from Eurostat. And next a related but conceptually more difficult thing with movavg(): If I restrict the sample to non-missing values, I can apply movavg, but the results are misleading, because gretl "forgets" that this is not really a contiguous time series anymore. (The fourth non-missing lagged value for example can be much farther in the past than four periods.) Intuitively I'd say that such "intertemporal" functions should require time-series data and not work on "undated" sub-samples, but OTOH I know that such "undated" sub-samples are often the outcome of sample restrictions that you cannot avoid and where it would be annoying if suddenly movavg() & co. didn't work anymore. So no concrete suggestion here, just food for thought. thanks, sven