ARIMA SAS Vs R Series
I am testing SAS and R with time series.
I have this code in R
ARIMA (1,1,0) (0,1,1)
ar1_ma12noint<-arima(qxts, order = c(1,1,0),seasonal = list(order = c(0,1, 1), period = 12),
include.mean = FALSE )
ar1_ma12noint
(1-pnorm(abs(ar1_ma12noint$coef)/sqrt(diag(ar1_ma12noint$var.coef))))*2
And this code is in SAS,
proc arima data= serie.diff12_r plots(unpack)=series(corr crosscorr);
identify var=pasajeros nlag=60 ;
estimate p=(1) q=(12) noint ;
run;
EDIT: SPSS shows the same scoring parameter as SAS.
i has the same model in both of them, but
R shows these evaluation parameters:
Coefficients:
ar1 sma1
-0.353 -0.498
se 0.082 0.068
And SAS,
MA1,1 0.48528 0.08367 5.80 <.0001 12
AR1,1 -0.34008 0.08666 -3.92 0.0001 1
I am wondering why the score is different from the two programs. I mean to sing for the seasonal setting ma.
thanks everyone!
EDIT: I think R is showing a moving average model with changing chant.
The question is close!
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1 answer
Two things:
- Your R model uses simple and seasonal distinction, whereas your SAS model does not
- SAS uses conditional least squares by default, while R uses conditional least squares to initialize ML estimates.
Setting ML estimates and adding order differences (1 12)
should give the same results:
proc arima data= serie.diff12_r plots(unpack)=series(corr crosscorr);
identify var=pasajeros(1 12) nlag=60 ;
estimate p=(1) q=(12) noint method=ml;
run;
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