HI all,
I am working with non stationary HF data . The source of stationnarity appers to by a  seasonal patern.
The ordinary gretl functions to deal with non stationary patterns are diff sdiff , while the corresponding  diff function is hfdiff/hfldiff.
 In the midas case what are the corresponding function to deal with seasonality? 

If I understand your problem correctly, then sdiff should still be OK for you, even in the MIDAS case.

For example, in the gdp_midas.gdt example dataset that comes with gretl, industrial production is a monthly variable, and monthly is the "high" frequency, so it comes as three Midas-series, namely indpro_m1, indpro_m2, indpro_m3. But the "core" or "low" frequency of the dataset is quarterly. So if you apply sdiff() to, say, indpro_m1, then the obs for, say, January 2010 is embedded in that series in the 2010Q1 low-freq period. Going back 4 quarters then gives you exactly what you want, January 2009 within 2009Q1.

So:

<hansl>

open gdp_midas.gdt

list L = indpro*

list direct = sdiff(L)

# manual comparison

series indirectM1 = indpro_m1 - indpro_m1(-$pd) # $pd is 4 here

# check the equality:

eval sum(abs(sd_indpro_m1 - indirectM1))

</hansl>

However, it seems that you have to re-define the result from sdiff() (the "direct" list here) explicitly as a MIDAS list again (setinfo direct --midas), for example for plotting purposes, which is a slight nuisance.

cheers

sven