10:17 a.m.
Dear all:
I have three questions.
1: Why is the outcome of X-12-ARIMA model almost the same as Seasonal ARIMA. For example,
there are two models with the same lags of AR and MA, namely X-12-ARIMA(1,1,0)(1,1,1) and
ARIMA(1,1,0)(1,1,1).
The coefficient of AR(1)、SAR(1)、SMA(1) of X-12-ARIMA(1,1,0)(1,1,1) is
0.853261、1.774953、0.114786.
The coefficient of AR(1)、SAR(1)、SMA(1) of Seasonal ARIMA(1,1,0)(1,1,1) is
0.853332、1.774762、0.114335
AIC of X-12-ARIMA(1,1,0)(1,1,1) is 2487.968
AIC of ARIMA(1,1,0)(1,1,1) is 2487.942
MAPE of out of sample of X-12-ARIMA(1,1,0)(1,1,1) is 4.7894
MAPE of out of sample of Seasonal ARIMA(1,1,0)(1,1,1) is 4.7326
This two models are almost the same. Other lags of AR and MA have the same situation. But
there is a few exceptions. For example, X-12-ARIMA(1,1,2)(2,1,0) and Seasonal
ARIMA(1,1,2)(2,1,0) may have different outcome.
2: I choose the options of Model/Time series/ARIMA/Using X-12-ARIMA to run the X-12-ARIMA
model. Is the set of equation of X-12-ARIMA in gretl the same as general model of RegARIMA
in X-12-ARIMA – Reference Manual, Version 0.3. (U.S. Census Bureau)?
I can not see the outcome of any seasonality adjusting regression variables(such as
length-of-month、level shift and so on).
3. Is there any relationship between the option of Model/Time series/ARIMA/Include a
constant and trend constant in regARIMA? Can I not choose the option of Model/Time
series/ARIMA/Include a constant when runing X-12-ARIMA?
Thanks a lot
7:19 p.m.
I do not have copies of either the Gretl or X-12-ARIMA manuals to hand
so you will have to look up details yourself. I would comment on your
questions as follows -
1. It is likely that any two econometric packages will produce
different results for an ARIMA estimation. Packages often use
different algorithms, or different convergence criteria. I suspect
that if the model fits the data well the answers will be very similar.
If you are estimating a model that does not fit well you are more
likely to get different answers. If you read the manual and verify
that the programs use the same algorithm and can adjust the
convergence criteria then you should be able to get the same answer.
2.You can not get these variables unless you specify them somewhere.
If you call the regarima model using X12 itself you have the option
of including explanatory variables such as as number of working days,
day of week effects, length of month etc. X12 offers a large number
of options that you can access using the x12 spec file. You could of
course define these variables within gretl and achieve the same
results with native gretl or calling x12 routines from Gretl.
X12-ARIMA has a lot of options and it might be worth your while to
learn to use it. Gretl does ease the preparation of X12-ARIMA spec
files.
Gretl writes fairly simple spec files when it calls x12-ARIMA. You
can amend these spec files manually to add various other X!2-ARIMA
options and use the amended spec file in the X12-ARIMA program. This
would enable you to use the full facilities of the REG-ARIMA routines.
3) I am afraid that I do not understand your question. What are you
trying to estimate? Is your main interest in estimation or seasonal
adjustment?
I hope that this is of some help.
John
2011/1/8 不提供 不提供 <dear.sam(a)livemail.tw>:
Dear all:
I have three questions.
1: Why is the outcome of X-12-ARIMA model almost the same as Seasonal ARIMA.
For example, there are two models with the same lags of AR and MA, namely
X-12-ARIMA(1,1,0)(1,1,1) and ARIMA(1,1,0)(1,1,1).
The coefficient of AR(1)、SAR(1)、SMA(1) of X-12-ARIMA(1,1,0)(1,1,1) is
0.853261、1.774953、0.114786.
The coefficient of AR(1)、SAR(1)、SMA(1) of Seasonal ARIMA(1,1,0)(1,1,1) is
0.853332、1.774762、0.114335
AIC of X-12-ARIMA(1,1,0)(1,1,1) is 2487.968
AIC of ARIMA(1,1,0)(1,1,1) is 2487.942
MAPE of out of sample of X-12-ARIMA(1,1,0)(1,1,1) is 4.7894
MAPE of out of sample of Seasonal ARIMA(1,1,0)(1,1,1) is 4.7326
This two models are almost the same. Other lags of AR and MA have the same
situation. But there is a few exceptions. For example,
X-12-ARIMA(1,1,2)(2,1,0) and Seasonal ARIMA(1,1,2)(2,1,0) may have
different outcome.
2: I choose the options of Model/Time series/ARIMA/Using X-12-ARIMA to run
the X-12-ARIMA model. Is the set of equation of X-12-ARIMA in gretl the same
as general model of RegARIMA in X-12-ARIMA – Reference Manual, Version 0.3.
(U.S. Census Bureau)?
I can not see the outcome of any seasonality adjusting regression
variables(such as length-of-month、level shift and so on).
3. Is there any relationship between the option of Model/Time
series/ARIMA/Include a constant and trend constant in regARIMA? Can I not
choose the option of Model/Time series/ARIMA/Include a constant when runing
X-12-ARIMA?
Thanks a lot
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--
John C Frain
Economics Department
Trinity College Dublin
Dublin 2
Ireland
www.tcd.ie/Economics/staff/frainj/home.html
mailto:frainj@tcd.ie
mailto:frainj@gmail.com
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