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 > > _______________________________________________ > Gretl-users mailing list > Gretl-users(a)lists.wfu.edu > http://lists.wfu.edu/mailman/listinfo/gretl-users > -- John C Frain Economics Department Trinity College Dublin Dublin 2 Ireland www.tcd.ie/Economics/staff/frainj/home.html mailto:frainj(a)tcd.ie mailto:frainj(a)gmail.com
