Current scaling factor makes mean ~10
with 1 196 sample mean is ~10^-8
Hence, scaling factor ~10^18
provided sd was about 1
it will be huge and x1 and x2 becomes practically constants=> zero
caeffs
Use --x-12-arima
add 1, 10, 10^5 to dep var and compare results
--x-12-arima definitely use shifting
a good variant to use (y-mean(y))+1 and then restore const
in terms of original y
also desirable to standardize each x and then restore
everything in terms of original variables
Note that all z-statistics but for the one at const are invariant
Oleh
PS I do not know what do do with missing constant yet
On Sun, 28 Oct 2018, Riccardo (Jack) Lucchetti wrote:
> On Fri, 26 Oct 2018, oleg_komashko@ukr.net wrote:
>
>>> Scrub the redundant "const" and see what happens.
>> I had scrubbed before the first message
>>
>> I think this is because of 197 use only scaling
>> to mean(scaled_y) = 10 and does not use centering
>
> Weird as it may seem, the problem arises because of subsampling. if TROUBLE
> == 1, the gradient is not computed correctly (as you can see from the BFGS
> iterations).
>
> <hansl>
> open bad_data.gdt
>
> TROUBLE = 0
> if TROUBLE
> smpl 1 194
> endif
> arima 3 0 0; 1 0 0; diff_series const y_one y_two --verbose
> </hansl>
Hmm, excluding the last 6 observations makes a huge difference to the
scaling factor applied to diff_series. I'll take a closer look when I
get time later today.
Allin
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