为什么auto.arima（）和Arima（）不同？

Question

我拟合模型的函数auto.arima()，那我就按功能Arima()与同型号的再次配合，但我得到不同的结果。

通过auto.arima()：

> a<-c(90,88,96,110,105,128,119,117,155,135,138,127,156,168,145,160,180,175,189,166,184) 
> chuoi<-ts(a,frequency=1,start=c(1990)) 
> auto.arima(chuoi) 
Series: chuoi 
ARIMA(2,1,0) with drift   

Coefficients: 
      ar1  ar2 drift 
     -0.7075 -0.4648 4.7897 
s.e. 0.1930 0.1972 1.3689 

sigma^2 estimated as 163.1: log likelihood=-79.7 
AIC=167.39 AICc=170.06 BIC=171.38 

通过Arima()与同型号，使用的所有方法 “CSS-ML”， “ML” 和 “CSS”：

> fit210<-Arima(chuoi,c(2,1,0),method="ML") 
> fit210 
Series: chuoi 
ARIMA(2,1,0)      

Coefficients: 
      ar1  ar2 
     -0.4670 -0.1928 
s.e. 0.2162 0.2201 

sigma^2 estimated as 244.2: log likelihood=-83.48 
AIC=172.96 AICc=174.46 BIC=175.95 
> fit210<-Arima(chuoi,c(2,1,0),method="CSS") 
> fit210 
Series: chuoi 
ARIMA(2,1,0)      

Coefficients: 
      ar1  ar2 
     -0.4876 -0.2111 
s.e. 0.2196 0.2304 

sigma^2 estimated as 268.3: part log likelihood=-84.3 
> fit210<-Arima(chuoi,c(2,1,0),method="CSS-ML") 
> fit210 
Series: chuoi 
ARIMA(2,1,0)      

Coefficients: 
      ar1  ar2 
     -0.4671 -0.1928 
s.e. 0.2162 0.2201 

sigma^2 estimated as 244.2: log likelihood=-83.48 
AIC=172.96 AICc=174.46 BIC=175.95 

很显然，我有不同的系数AR （1），ar（2）。那么，函数auto.arima()如何计算系数ar（1），ar（2）？

Answer 1

你的第一个模型包括漂移，你需要用

Arima(chuoi,c(2,1,0),include.drift = TRUE) 

这两个是相同的运行它：

auto.arima(chuoi) 
Arima(chuoi,c(2,1,0),include.drift = TRUE) # default model, but with drift 

输出：

> auto.arima(chuoi) 
Series: chuoi 
ARIMA(2,1,0) with drift   

Coefficients: 
      ar1  ar2 drift 
     -0.7075 -0.4648 4.7897 
s.e. 0.1930 0.1972 1.3689 

sigma^2 estimated as 163.1: log likelihood=-79.7 
AIC=167.39 AICc=170.06 BIC=171.38 



> Arima(chuoi,c(2,1,0),include.drift = T) 
Series: chuoi 
ARIMA(2,1,0) with drift   

Coefficients: 
      ar1  ar2 drift 
     -0.7075 -0.4648 4.7897 
s.e. 0.1930 0.1972 1.3689 

sigma^2 estimated as 163.1: log likelihood=-79.7 
AIC=167.39 AICc=170.06 BIC=171.38 
>