Hi
I was assuming that the estimated function included logged regressors
compared with Eqn 2. which is a semilog function.Nor is (2) in any sense an
ARIMA specification unless one assumes it to be 0,0,1
Given that the adjustment would appear to be very small, if indeed it would
take that form in an ARIMA model spec. one might ask is it worth the
effort?
On Sun, 5 Jan 2025, 13:04 Sven Schreiber, <sven.schreiber(a)fu-berlin.de>
wrote:
Am 05.01.2025 um 02:13 schrieb Brian Revell:
Take the antilog of the log{y}=z transformed actual and fitted or forecast
values from the ARIMA model, which if in natural logs will be EXP(z).
Brian, as explained (inter alia) by Dave Giles in the link that Allin
gave, this is not completely correct.
However, ... (see further down)
On Sat, 4 Jan 2025, 23:59 Cottrell, Allin, <cottrell(a)wfu.edu> wrote:
> On Sat, Jan 4, 2025 at 2:55 PM <dbrilakis(a)yahoo.gr> wrote:
> >
> > Hi, I found that my data become stationary (after differentiate) the
> log data with best ARIMA(p,d,q) How do I rebuilt the ARIMA forecast to the
> origina scale before log?
>
> There's a standard means of converting from a forecast or fitted value
> of log(y) to that of y itself, if the error term is reckoned to be
> normal, plus some variations on the theme. Dave Giles has quite a nice
> discussion of the point: see
>
https://davegiles.blogspot.com/2014/12/s.html
One interesting point on that blog page is also the comment by user "
Daumantas", citing BÅRDSEN, G. & LÜTKEPOHL, H. 2011. Forecasting levels
of log variables in vector autoregressions. International Journal of
Forecasting, 27, 1108-1115: '...if specification and estimation
uncertainty are taken into account [...] in practice, using the exponential
of the log forecast is preferable to using the optimal forecast." [...]
(Log-normality is assumed...)'
In that sense Brian's simple recipe would not be misguided.
Apart from that, this natural question has been asked a couple of years
ago, see:
https://gretlml.univpm.it/hyperkitty/list/gretl-users@gretlml.univpm.it/t...
For convenience, I'm reproducing the script that Jack provided back then:
<hansl> open bjg.gdt --quiet series insample = t < "1960:3" series f =
NA
smpl insample == 1 --restrict arima 0 1 1 ; 0 1 1 ; lg fcast
--out-of-sample matrix F = $fcast + 0.5 * $fcse.^2 smpl insample == 0
--restrict --replace f = exp(F) setinfo f --graph-name="forecast" smpl full
gnuplot g f --time-series --with-lines --output=display
</hansl>
Actually, I'm not sure whether in this script $fcse should be used as an
estimator of the theoretical sigma, or perhaps rather the model accessor
$sigma.
Again, all this assumes (log)normal errors, which might be completely
wrong for the given data.
Maybe we could offer something more automated if the dependent variable of
a model is recognized as being the log of something. As this is becoming
increasingly technical, I will post something to follow up on this to the
development list.
cheers
sven
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