[英]ARIMA series SAS vs R
I am testing SAS and R with time series. 我正在测试SAS和R的时间序列。
I have this code in R 我在R中有这个代码
ARIMA (1,1,0) (0,1,1) 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 in SAS, 这个代码在SAS中,
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 same estimate parameter than SAS. 编辑:SPSS显示与SAS相同的估计参数。
i have same model in both of them but 我有两个相同的模型但是
R shows this estimate parameters: R显示了这个估计参数:
Coefficients:
ar1 sma1
-0.353 -0.498
se 0.082 0.068 se 0.082 0.068
And SAS, 和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 estimate is different beetween two programs. 我想知道为什么两个程序之间的估计是不同的。 I mean the sing for seasonal ma parameter.
我的意思是唱季节性的ma参数。
thanks for all! 谢谢大家!
EDIT: i think R shows moving average model with change sing. 编辑:我认为R显示移动平均模型与变化唱歌。
Question is close! 问题很接近!
Two things: 两件事情:
Specifying ML estimates and adding differencing of orders (1 12)
should produce the same results: 指定ML估计并添加订单差异
(1 12)
应产生相同的结果:
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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