Am 31.01.2024 um 11:47 schrieb d.laloun...@minfin.gr:
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
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