You can use armax_auto (see time series menu in Gretl GUI). The function

has a drawback: the cycle crushes on encountering a unit root
in MA part of the model
Also you can use auto.arima{forecast} in R
My experience tells it would be better to determine the order of
integration (use unit root tests) beforehand and use, say "d=1"
in auto.arima (type ?auto.arima in R console)

--- Оригінальне повідомлення ---
Від кого: "Tim Nall" <tnall.ling@gmail.com>
Дата: 26 квітня 2014, 03:53:32

All,

Please forgive my simple questions. For ARIMA modelling, using the ACF
and PACF to determine the pdq parameters requires subjective judgment
based on experience. Answers are seldom clear-cut. As the subject
header says, can a somewhat more objective path be found in quickly
running through ARIMA models, varying parameters, acquiring AIC, BIC,
HQC, and comparing? I don't think the time-series-->VAR lag selection
option gets at precisely the same thing. Thank you TMN
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